{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import re\n",
    "import joblib\n",
    "from scipy.interpolate import interp1d\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import savgol_filter\n",
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_steps = [0, 0.1, 1, 20, 40, 60, 120, 180, 240, 300, 360, 480, 600, 720, 840, 960, 1080, 1200, 1500, 1800, 2400, 3000, 3600, 4500, 5400, 6300, 7200, 9000, 10800, 14400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"Step 3_RF_model_conc_normalized_n_estimators=120.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mechanism_(0, 146)', 'Mechanism_(0, 147)', 'Mechanism_(0, 154)',\n",
       "       'Mechanism_(0, 155)', 'Mechanism_(1, 146)', 'Mechanism_(1, 147)',\n",
       "       'Mechanism_(1, 154)', 'Mechanism_(1, 155)', 'Mechanism_(101, 126)',\n",
       "       'Mechanism_(101, 162)',\n",
       "       ...\n",
       "       'Mechanism_(97, 128)', 'Mechanism_(97, 166)', 'Mechanism_(97, 167)',\n",
       "       'Mechanism_(97, 181)', 'Mechanism_(97, 220)', 'Mechanism_(97, 221)',\n",
       "       'Mechanism_(97, 338)', 'Mechanism_(97, 386)', 'Mechanism_(97, 404)',\n",
       "       'Mechanism_(97, 452)'],\n",
       "      dtype='object', length=456)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df = pd.read_hdf('y_df_oh.h5')\n",
    "\n",
    "y_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>Mechanism_(0, 146)</th>\n",
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       "         Mechanism_(0, 146)  Mechanism_(0, 147)  Mechanism_(0, 154)  \\\n",
       "0                     False               False               False   \n",
       "1                     False               False               False   \n",
       "2                     False               False               False   \n",
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       "...                     ...                 ...                 ...   \n",
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       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
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       "         Mechanism_(0, 155)  Mechanism_(1, 146)  Mechanism_(1, 147)  \\\n",
       "0                     False               False               False   \n",
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       "\n",
       "         Mechanism_(1, 154)  Mechanism_(1, 155)  Mechanism_(101, 126)  \\\n",
       "0                     False               False                 False   \n",
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       "2                     False               False                 False   \n",
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       "...                     ...                 ...                   ...   \n",
       "1933581               False               False                 False   \n",
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       "\n",
       "         Mechanism_(101, 162)  ...  Mechanism_(97, 128)  Mechanism_(97, 166)  \\\n",
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       "1933584                 False  ...                False                False   \n",
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       "\n",
       "         Mechanism_(97, 167)  Mechanism_(97, 181)  Mechanism_(97, 220)  \\\n",
       "0                      False                False                False   \n",
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       "\n",
       "         Mechanism_(97, 221)  Mechanism_(97, 338)  Mechanism_(97, 386)  \\\n",
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       "...                      ...                  ...                  ...   \n",
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       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 404)  Mechanism_(97, 452)  \n",
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       "4                      False                False  \n",
       "...                      ...                  ...  \n",
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       "\n",
       "[1933586 rows x 456 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
  },
  {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SM -&gt; IMP1</td>\n",
       "      <td>SM,IMP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SM + R -&gt; IMP1</td>\n",
       "      <td>SM,R,IMP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SM -&gt; IMP1 + INT1</td>\n",
       "      <td>SM,IMP1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Reactions  Step  SM_left  C_left  B_left  P_right  IMP1_right  \\\n",
       "0                    2     1        1       0       0        0           0   \n",
       "1                    2     1        1       0       0        0           0   \n",
       "2                    2     1        1       0       0        0           1   \n",
       "3                    2     1        1       0       0        0           1   \n",
       "4                    2     1        1       0       0        0           1   \n",
       "\n",
       "   INT1_right  C_right  B_right  R_left  P_left  INT1_left  IMP2_right  \\\n",
       "0           1        0        0       0       0          0           0   \n",
       "1           1        0        0       1       0          0           0   \n",
       "2           0        0        0       0       0          0           0   \n",
       "3           0        0        0       1       0          0           0   \n",
       "4           1        0        0       0       0          0           0   \n",
       "\n",
       "     Reaction Format Initial Compounds  \n",
       "0         SM -> INT1                SM  \n",
       "1     SM + R -> INT1              SM,R  \n",
       "2         SM -> IMP1           SM,IMP1  \n",
       "3     SM + R -> IMP1         SM,R,IMP1  \n",
       "4  SM -> IMP1 + INT1           SM,IMP1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database = pd.read_csv(\"database_two_reactions.csv\")\n",
    "df_database = database.drop(database.columns[0], axis=1)\n",
    "df_database.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(590, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_database.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.read_csv(\"../two_reactions_022624.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
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       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_10800s</th>\n",
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       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
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       "      <td>0.004704</td>\n",
       "      <td>76</td>\n",
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       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(105, 197)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>273</td>\n",
       "      <td>0.016741</td>\n",
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       "      <td>149</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(105, 197)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>273</td>\n",
       "      <td>0.483504</td>\n",
       "      <td>39</td>\n",
       "      <td>0.079969</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(105, 197)</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>273</td>\n",
       "      <td>2.633989</td>\n",
       "      <td>154</td>\n",
       "      <td>1.648038</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.001348</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(105, 197)</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 258 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Temperature        A1  Ea1          A2  Ea2  A3  Ea3  A4  Ea4  \\\n",
       "0           0          273  0.034204    1    0.095650  182   0    0   0    0   \n",
       "1           1          273  0.004704   76    6.225530   65   0    0   0    0   \n",
       "2           2          273  0.016741  189  350.310546  149   0    0   0    0   \n",
       "3           3          273  0.483504   39    0.079969   10   0    0   0    0   \n",
       "4           4          273  2.633989  154    1.648038  127   0    0   0    0   \n",
       "\n",
       "   ...  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  Slow_rxn1  \\\n",
       "0  ...      0.007172      0.006560          0            0          1   \n",
       "1  ...      0.000466      0.000450          0            0          1   \n",
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       "3  ...      0.009851      0.008435          0            1          0   \n",
       "4  ...      0.001653      0.001348          0            1          0   \n",
       "\n",
       "   Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "0          0            0          1   \n",
       "1          0            1          0   \n",
       "2          1            0          0   \n",
       "3          0            0          1   \n",
       "4          0            1          0   \n",
       "\n",
       "                                      Reaction_order   Mechanism  \n",
       "0  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (105, 197)  \n",
       "1  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (105, 197)  \n",
       "2  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (105, 197)  \n",
       "3  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (105, 197)  \n",
       "4  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (105, 197)  \n",
       "\n",
       "[5 rows x 258 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = data_df.drop(data_df.columns[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 257)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(105, 197)</td>\n",
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       "      <td>(105, 197)</td>\n",
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       "</table>\n",
       "<p>5 rows × 257 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature        A1  Ea1          A2  Ea2  A3  Ea3  A4  Ea4  cSM_0s  ...  \\\n",
       "0          273  0.034204    1    0.095650  182   0    0   0    0     0.3  ...   \n",
       "1          273  0.004704   76    6.225530   65   0    0   0    0     0.3  ...   \n",
       "2          273  0.016741  189  350.310546  149   0    0   0    0     0.3  ...   \n",
       "3          273  0.483504   39    0.079969   10   0    0   0    0     0.3  ...   \n",
       "4          273  2.633989  154    1.648038  127   0    0   0    0     0.3  ...   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  Slow_rxn1  Fast_rxn2  \\\n",
       "0      0.007172      0.006560          0            0          1          0   \n",
       "1      0.000466      0.000450          0            0          1          0   \n",
       "2      0.000095      0.000088          0            0          1          1   \n",
       "3      0.009851      0.008435          0            1          0          0   \n",
       "4      0.001653      0.001348          0            1          0          0   \n",
       "\n",
       "   Medium_rxn2  Slow_rxn2                                     Reaction_order  \\\n",
       "0            0          1  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...   \n",
       "1            1          0  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...   \n",
       "2            0          0  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...   \n",
       "3            0          1  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...   \n",
       "4            1          0  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...   \n",
       "\n",
       "    Mechanism  \n",
       "0  (105, 197)  \n",
       "1  (105, 197)  \n",
       "2  (105, 197)  \n",
       "3  (105, 197)  \n",
       "4  (105, 197)  \n",
       "\n",
       "[5 rows x 257 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolation_savgol(original_time_course, original_time):\n",
    "    original_time_course_copy = original_time_course.copy()\n",
    "    original_time_copy = original_time.copy()\n",
    "    savgol_curve = savgol_filter(original_time_course_copy, window_length=5, polyorder=1)\n",
    "    interp_func = interp1d(original_time_copy, savgol_curve, kind='linear', fill_value=\"extrapolate\")\n",
    "    new_time_course = interp_func(time_steps).tolist()\n",
    "    return new_time_course\n",
    "\n",
    "# Normalization\n",
    "def normalization(df_conc):\n",
    "    # Create a MinMaxScaler instance\n",
    "    scaler = MinMaxScaler()\n",
    "\n",
    "    # Transpose the DataFrame so that rows become columns for scaling\n",
    "    x_df_transposed = df_conc.T\n",
    "\n",
    "    # Scale the entire transposed DataFrame\n",
    "    scaled_data = scaler.fit_transform(x_df_transposed)\n",
    "\n",
    "    # Transpose the scaled data back to the original orientation\n",
    "    x_df = pd.DataFrame(scaled_data.T, columns=df_conc.columns, index=df_conc.index)\n",
    "    return x_df\n",
    "\n",
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 0.8\n",
    "\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(df_raw, n_columns, n_experiments, model):\n",
    "    split_dfs = np.array_split(df_raw, n_experiments, axis=1)\n",
    "    res_probilities = {}\n",
    "    res_prediction = {}\n",
    "    res_original_data = {}\n",
    "    res_interpolated_data = {}\n",
    "\n",
    "    for idx, df in enumerate(split_dfs):\n",
    "        df = df.dropna(how='all')\n",
    "        data_dict = {}\n",
    "        for col_idx in range(n_columns + 1):\n",
    "            column_name = df.columns[col_idx]\n",
    "            data_dict[column_name.split()[0]] = df.iloc[:, col_idx].tolist()\n",
    "\n",
    "        new_time_course = {}\n",
    "        interpolated_data = {}\n",
    "        for i in data_dict:\n",
    "            if i == \"Time\":\n",
    "                original_time = [value * 60 for value in data_dict[i]]\n",
    "            else:\n",
    "                new_time_course[i] = interpolation_savgol(data_dict[i], original_time)\n",
    "\n",
    "        original_data = data_dict.copy()\n",
    "        original_data['Time'] = original_time\n",
    "        res_original_data[idx] = original_data\n",
    "        interpolated_data['Time'] = time_steps\n",
    "        interpolated_data.update(new_time_course)\n",
    "        res_interpolated_data[idx] = interpolated_data\n",
    "\n",
    "        species_list = list(new_time_course.keys())\n",
    "        dfs = {}\n",
    "        for species in species_list:\n",
    "            df_species  = pd.DataFrame([new_time_course[species]], columns=[species + '_' + str(i) for i in range(len(new_time_course[species]))])\n",
    "            dfs['df_' + species] = df_species\n",
    "        \n",
    "        num_rows = 1\n",
    "        num_columns = 1 * len(time_steps)\n",
    "        zero_df = pd.DataFrame(0, index=range(num_rows), columns=range(num_columns))\n",
    "        species_list_full = ('SM', 'R', 'B', 'C', 'P', 'IMP1', 'IMP2', 'INT1')\n",
    "        dfs_keys = [name.split('_')[1] for name in list(dfs.keys())]\n",
    "        df_conc = dfs['df_SM']\n",
    "        for j in range(1, len(species_list_full)):\n",
    "            if species_list_full[j] in dfs_keys:\n",
    "                df_conc = pd.concat([df_conc, dfs['df_' + species_list_full[j]]], axis=1)\n",
    "            else:\n",
    "                df_conc = pd.concat([df_conc, zero_df], axis=1)\n",
    "\n",
    "        df_conc[df_conc < 0] = 0\n",
    "        x_df = normalization(df_conc)\n",
    "        pd.set_option('display.max_colwidth', None)\n",
    "        x = x_df.to_numpy()\n",
    "\n",
    "        y_predicted_proba = model.predict_proba(x)\n",
    "\n",
    "        for idx_proba, i in enumerate(y_predicted_proba):\n",
    "            grouping, probabilities = generate_predicted_grouping(i)\n",
    "        mechanism_predicted = []\n",
    "        for idx_group, k in enumerate(grouping):\n",
    "            pattern = r'\\((\\d+), (\\d+)\\)'\n",
    "            match = re.search(pattern, y_df.columns[k-1])\n",
    "            idx_elementary_step_1 = int(match.group(1))\n",
    "            idx_elementary_step_2 = int(match.group(2))\n",
    "            elementary_step_1 = df_database.iloc[idx_elementary_step_1, -2]\n",
    "            elementary_step_2 = df_database.iloc[idx_elementary_step_2, -2]\n",
    "            mechanism_predicted.append([elementary_step_1, elementary_step_2])\n",
    "        res_probilities[idx] = probabilities\n",
    "        res_prediction[idx] = mechanism_predicted\n",
    "        \n",
    "    return res_probilities, res_prediction, res_original_data, res_interpolated_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(res_original_data, res_interpolated_data, output_dir='plots/'):\n",
    "\n",
    "    # Define the rows you want to plot\n",
    "    idx = range(0, len(res_original_data))\n",
    "    # print(idx)\n",
    "\n",
    "    # Define the variables to plot\n",
    "    variables = list(res_original_data[0].keys())\n",
    "    if 'Time' in variables:\n",
    "        variables.remove('Time')\n",
    "\n",
    "    # Set the number of subplots per row\n",
    "    subplots_per_row = len(variables)\n",
    "\n",
    "    # Create the output directory if it doesn't exist\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "    \n",
    "    # Loop through each row\n",
    "    for i in idx:\n",
    "        # Create a new figure for each row\n",
    "        # plt.figure(figsize=(15, 5))\n",
    "        plt.figure(figsize=(3 * subplots_per_row, 4.5))\n",
    "\n",
    "        # Loop through each variable\n",
    "        for j, variable in enumerate(variables, start=1):\n",
    "            plt.subplot(2, subplots_per_row, j)\n",
    "            \n",
    "            # Extract the relevant data\n",
    "            original_data = res_original_data[i]\n",
    "            interpolated_data = res_interpolated_data[i]\n",
    "\n",
    "            # Plot the original data\n",
    "            plt.scatter(original_data['Time'], original_data[variable], label='Experimental Data')\n",
    "\n",
    "            # Plot the interpolated data\n",
    "            plt.plot(interpolated_data['Time'], interpolated_data[variable], label='Interpolated Data') #, color='orange')\n",
    "\n",
    "            # Set plot title\n",
    "            plt.title(variable, fontsize=14, y = 1.05)\n",
    "\n",
    "            # Set axis labels and axis size\n",
    "            plt.xlabel('Time [s]', fontsize=12, labelpad=10)\n",
    "            plt.ylabel('Concentration [mM]', fontsize=12, labelpad=10)\n",
    "\n",
    "            # Set axis size\n",
    "            plt.xticks(fontsize=12)\n",
    "            plt.yticks(fontsize=12)\n",
    "            plt.xlim(0, 30000)\n",
    "\n",
    "        # plt.legend()\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        # plt.savefig(os.path.join(output_dir, f'Case study 2_plot_{i}.png'))\n",
    "        # plt.savefig(os.path.join(output_dir, f'Case study 1_plot_{i}.png'))\n",
    "        plt.show()\n",
    "        plt.close()    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Case Studies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.1 Case Study 1: (SM + R + B -> P + B, P + R + B -> IMP2 + B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (min)</th>\n",
       "      <th>SM (mM)</th>\n",
       "      <th>R (mM)</th>\n",
       "      <th>B (mM)</th>\n",
       "      <th>P (mM)</th>\n",
       "      <th>IMP2 (mM)</th>\n",
       "      <th>Time (min).1</th>\n",
       "      <th>SM (mM).1</th>\n",
       "      <th>R (mM).1</th>\n",
       "      <th>B (mM).1</th>\n",
       "      <th>...</th>\n",
       "      <th>R (mM).3</th>\n",
       "      <th>B (mM).3</th>\n",
       "      <th>P (mM).3</th>\n",
       "      <th>IMP2 (mM).3</th>\n",
       "      <th>Time (min).4</th>\n",
       "      <th>SM (mM).4</th>\n",
       "      <th>R (mM).4</th>\n",
       "      <th>B (mM).4</th>\n",
       "      <th>P (mM).4</th>\n",
       "      <th>IMP2 (mM).4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>204.902276</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>161.856866</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.808732</td>\n",
       "      <td>400.341006</td>\n",
       "      <td>405.261122</td>\n",
       "      <td>...</td>\n",
       "      <td>150.127877</td>\n",
       "      <td>354.603482</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.340173</td>\n",
       "      <td>200.170503</td>\n",
       "      <td>810.522244</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>195.316856</td>\n",
       "      <td>195.068569</td>\n",
       "      <td>156.925435</td>\n",
       "      <td>4.931431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>190.890576</td>\n",
       "      <td>386.708428</td>\n",
       "      <td>391.848109</td>\n",
       "      <td>...</td>\n",
       "      <td>146.462487</td>\n",
       "      <td>350.938091</td>\n",
       "      <td>3.665391</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>198.368989</td>\n",
       "      <td>193.778859</td>\n",
       "      <td>804.130600</td>\n",
       "      <td>6.391644</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>193.205762</td>\n",
       "      <td>191.062516</td>\n",
       "      <td>152.919383</td>\n",
       "      <td>8.937484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>178.387149</td>\n",
       "      <td>374.515929</td>\n",
       "      <td>380.100775</td>\n",
       "      <td>...</td>\n",
       "      <td>143.162274</td>\n",
       "      <td>347.637878</td>\n",
       "      <td>6.965603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>193.475999</td>\n",
       "      <td>188.029123</td>\n",
       "      <td>798.380864</td>\n",
       "      <td>12.141380</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>189.136680</td>\n",
       "      <td>187.090188</td>\n",
       "      <td>149.100460</td>\n",
       "      <td>12.756406</td>\n",
       "      <td>0.076703</td>\n",
       "      <td>45.0</td>\n",
       "      <td>168.634644</td>\n",
       "      <td>363.064995</td>\n",
       "      <td>369.365632</td>\n",
       "      <td>...</td>\n",
       "      <td>140.081119</td>\n",
       "      <td>344.556723</td>\n",
       "      <td>10.046758</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>188.044962</td>\n",
       "      <td>182.258922</td>\n",
       "      <td>792.984155</td>\n",
       "      <td>17.538089</td>\n",
       "      <td>0.186746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>184.308263</td>\n",
       "      <td>181.506256</td>\n",
       "      <td>143.673385</td>\n",
       "      <td>18.183482</td>\n",
       "      <td>0.155131</td>\n",
       "      <td>60.0</td>\n",
       "      <td>157.274370</td>\n",
       "      <td>352.731180</td>\n",
       "      <td>359.926661</td>\n",
       "      <td>...</td>\n",
       "      <td>136.747547</td>\n",
       "      <td>341.472213</td>\n",
       "      <td>13.131268</td>\n",
       "      <td>0.124531</td>\n",
       "      <td>60.0</td>\n",
       "      <td>183.226744</td>\n",
       "      <td>176.776735</td>\n",
       "      <td>787.639514</td>\n",
       "      <td>22.882730</td>\n",
       "      <td>0.255519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>178.798099</td>\n",
       "      <td>176.029932</td>\n",
       "      <td>138.446715</td>\n",
       "      <td>23.410152</td>\n",
       "      <td>0.279958</td>\n",
       "      <td>90.0</td>\n",
       "      <td>140.196092</td>\n",
       "      <td>333.413976</td>\n",
       "      <td>342.972747</td>\n",
       "      <td>...</td>\n",
       "      <td>130.618301</td>\n",
       "      <td>335.720556</td>\n",
       "      <td>18.882926</td>\n",
       "      <td>0.313325</td>\n",
       "      <td>90.0</td>\n",
       "      <td>172.559786</td>\n",
       "      <td>166.885405</td>\n",
       "      <td>778.352579</td>\n",
       "      <td>32.169665</td>\n",
       "      <td>0.557716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>90</td>\n",
       "      <td>169.872599</td>\n",
       "      <td>165.467693</td>\n",
       "      <td>128.433527</td>\n",
       "      <td>33.423340</td>\n",
       "      <td>0.554484</td>\n",
       "      <td>120.0</td>\n",
       "      <td>123.539688</td>\n",
       "      <td>316.526618</td>\n",
       "      <td>329.043822</td>\n",
       "      <td>...</td>\n",
       "      <td>125.002322</td>\n",
       "      <td>330.392369</td>\n",
       "      <td>24.211112</td>\n",
       "      <td>0.457221</td>\n",
       "      <td>120.0</td>\n",
       "      <td>162.526515</td>\n",
       "      <td>157.531183</td>\n",
       "      <td>769.754381</td>\n",
       "      <td>40.767863</td>\n",
       "      <td>0.935728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120</td>\n",
       "      <td>160.071471</td>\n",
       "      <td>156.322690</td>\n",
       "      <td>119.990141</td>\n",
       "      <td>41.866725</td>\n",
       "      <td>0.905293</td>\n",
       "      <td>180.0</td>\n",
       "      <td>90.391875</td>\n",
       "      <td>287.665483</td>\n",
       "      <td>307.469761</td>\n",
       "      <td>...</td>\n",
       "      <td>115.051459</td>\n",
       "      <td>321.236542</td>\n",
       "      <td>33.366940</td>\n",
       "      <td>0.854739</td>\n",
       "      <td>180.0</td>\n",
       "      <td>147.751687</td>\n",
       "      <td>140.765584</td>\n",
       "      <td>754.819787</td>\n",
       "      <td>55.702456</td>\n",
       "      <td>1.851231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>180</td>\n",
       "      <td>144.996505</td>\n",
       "      <td>139.931777</td>\n",
       "      <td>105.351668</td>\n",
       "      <td>56.505199</td>\n",
       "      <td>1.781512</td>\n",
       "      <td>240.0</td>\n",
       "      <td>78.652102</td>\n",
       "      <td>264.798162</td>\n",
       "      <td>292.775243</td>\n",
       "      <td>...</td>\n",
       "      <td>106.451053</td>\n",
       "      <td>313.675200</td>\n",
       "      <td>40.928282</td>\n",
       "      <td>1.374271</td>\n",
       "      <td>240.0</td>\n",
       "      <td>133.390978</td>\n",
       "      <td>127.321575</td>\n",
       "      <td>743.323572</td>\n",
       "      <td>67.198672</td>\n",
       "      <td>2.825128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>240</td>\n",
       "      <td>128.274066</td>\n",
       "      <td>126.516406</td>\n",
       "      <td>93.892408</td>\n",
       "      <td>67.964458</td>\n",
       "      <td>2.759568</td>\n",
       "      <td>360.0</td>\n",
       "      <td>54.391199</td>\n",
       "      <td>230.406907</td>\n",
       "      <td>276.317682</td>\n",
       "      <td>...</td>\n",
       "      <td>91.104213</td>\n",
       "      <td>300.618299</td>\n",
       "      <td>53.985182</td>\n",
       "      <td>2.519241</td>\n",
       "      <td>360.0</td>\n",
       "      <td>116.290196</td>\n",
       "      <td>106.380087</td>\n",
       "      <td>726.675190</td>\n",
       "      <td>83.847054</td>\n",
       "      <td>4.971681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>360</td>\n",
       "      <td>111.662593</td>\n",
       "      <td>107.633848</td>\n",
       "      <td>78.726039</td>\n",
       "      <td>83.130827</td>\n",
       "      <td>4.617662</td>\n",
       "      <td>540.0</td>\n",
       "      <td>35.575146</td>\n",
       "      <td>197.284941</td>\n",
       "      <td>268.668260</td>\n",
       "      <td>...</td>\n",
       "      <td>75.013306</td>\n",
       "      <td>288.085865</td>\n",
       "      <td>66.517617</td>\n",
       "      <td>4.298477</td>\n",
       "      <td>540.0</td>\n",
       "      <td>98.684638</td>\n",
       "      <td>84.565823</td>\n",
       "      <td>711.001395</td>\n",
       "      <td>99.520848</td>\n",
       "      <td>8.041916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>480</td>\n",
       "      <td>95.049959</td>\n",
       "      <td>92.617630</td>\n",
       "      <td>67.676236</td>\n",
       "      <td>94.180631</td>\n",
       "      <td>6.600870</td>\n",
       "      <td>900.0</td>\n",
       "      <td>10.621228</td>\n",
       "      <td>162.395889</td>\n",
       "      <td>276.274403</td>\n",
       "      <td>...</td>\n",
       "      <td>56.663322</td>\n",
       "      <td>276.145417</td>\n",
       "      <td>78.458065</td>\n",
       "      <td>7.503245</td>\n",
       "      <td>900.0</td>\n",
       "      <td>63.799597</td>\n",
       "      <td>60.794506</td>\n",
       "      <td>698.351582</td>\n",
       "      <td>112.170662</td>\n",
       "      <td>13.602667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>720</td>\n",
       "      <td>73.266300</td>\n",
       "      <td>75.073525</td>\n",
       "      <td>56.626038</td>\n",
       "      <td>105.230829</td>\n",
       "      <td>9.847823</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>4.416116</td>\n",
       "      <td>143.970399</td>\n",
       "      <td>284.539708</td>\n",
       "      <td>...</td>\n",
       "      <td>47.567259</td>\n",
       "      <td>270.530196</td>\n",
       "      <td>84.073286</td>\n",
       "      <td>9.243666</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>47.572889</td>\n",
       "      <td>46.264808</td>\n",
       "      <td>692.586293</td>\n",
       "      <td>117.935951</td>\n",
       "      <td>17.984872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1200</td>\n",
       "      <td>57.834927</td>\n",
       "      <td>53.588914</td>\n",
       "      <td>44.145322</td>\n",
       "      <td>117.711544</td>\n",
       "      <td>14.349771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time (min)     SM (mM)      R (mM)      B (mM)      P (mM)  IMP2 (mM)  \\\n",
       "0            0  204.902276  200.000000  161.856866    0.000000   0.000000   \n",
       "1           10  195.316856  195.068569  156.925435    4.931431   0.000000   \n",
       "2           20  193.205762  191.062516  152.919383    8.937484   0.000000   \n",
       "3           30  189.136680  187.090188  149.100460   12.756406   0.076703   \n",
       "4           45  184.308263  181.506256  143.673385   18.183482   0.155131   \n",
       "5           60  178.798099  176.029932  138.446715   23.410152   0.279958   \n",
       "6           90  169.872599  165.467693  128.433527   33.423340   0.554484   \n",
       "7          120  160.071471  156.322690  119.990141   41.866725   0.905293   \n",
       "8          180  144.996505  139.931777  105.351668   56.505199   1.781512   \n",
       "9          240  128.274066  126.516406   93.892408   67.964458   2.759568   \n",
       "10         360  111.662593  107.633848   78.726039   83.130827   4.617662   \n",
       "11         480   95.049959   92.617630   67.676236   94.180631   6.600870   \n",
       "12         720   73.266300   75.073525   56.626038  105.230829   9.847823   \n",
       "13        1200   57.834927   53.588914   44.145322  117.711544  14.349771   \n",
       "\n",
       "    Time (min).1   SM (mM).1    R (mM).1    B (mM).1  ...    R (mM).3  \\\n",
       "0            0.0  202.808732  400.341006  405.261122  ...  150.127877   \n",
       "1           15.0  190.890576  386.708428  391.848109  ...  146.462487   \n",
       "2           30.0  178.387149  374.515929  380.100775  ...  143.162274   \n",
       "3           45.0  168.634644  363.064995  369.365632  ...  140.081119   \n",
       "4           60.0  157.274370  352.731180  359.926661  ...  136.747547   \n",
       "5           90.0  140.196092  333.413976  342.972747  ...  130.618301   \n",
       "6          120.0  123.539688  316.526618  329.043822  ...  125.002322   \n",
       "7          180.0   90.391875  287.665483  307.469761  ...  115.051459   \n",
       "8          240.0   78.652102  264.798162  292.775243  ...  106.451053   \n",
       "9          360.0   54.391199  230.406907  276.317682  ...   91.104213   \n",
       "10         540.0   35.575146  197.284941  268.668260  ...   75.013306   \n",
       "11         900.0   10.621228  162.395889  276.274403  ...   56.663322   \n",
       "12        1190.0    4.416116  143.970399  284.539708  ...   47.567259   \n",
       "13           NaN         NaN         NaN         NaN  ...         NaN   \n",
       "\n",
       "      B (mM).3   P (mM).3  IMP2 (mM).3  Time (min).4   SM (mM).4    R (mM).4  \\\n",
       "0   354.603482   0.000000     0.000000           0.0  204.340173  200.170503   \n",
       "1   350.938091   3.665391     0.000000          15.0  198.368989  193.778859   \n",
       "2   347.637878   6.965603     0.000000          30.0  193.475999  188.029123   \n",
       "3   344.556723  10.046758     0.000000          45.0  188.044962  182.258922   \n",
       "4   341.472213  13.131268     0.124531          60.0  183.226744  176.776735   \n",
       "5   335.720556  18.882926     0.313325          90.0  172.559786  166.885405   \n",
       "6   330.392369  24.211112     0.457221         120.0  162.526515  157.531183   \n",
       "7   321.236542  33.366940     0.854739         180.0  147.751687  140.765584   \n",
       "8   313.675200  40.928282     1.374271         240.0  133.390978  127.321575   \n",
       "9   300.618299  53.985182     2.519241         360.0  116.290196  106.380087   \n",
       "10  288.085865  66.517617     4.298477         540.0   98.684638   84.565823   \n",
       "11  276.145417  78.458065     7.503245         900.0   63.799597   60.794506   \n",
       "12  270.530196  84.073286     9.243666        1400.0   47.572889   46.264808   \n",
       "13         NaN        NaN          NaN           NaN         NaN         NaN   \n",
       "\n",
       "      B (mM).4    P (mM).4  IMP2 (mM).4  \n",
       "0   810.522244    0.000000     0.000000  \n",
       "1   804.130600    6.391644     0.000000  \n",
       "2   798.380864   12.141380     0.000000  \n",
       "3   792.984155   17.538089     0.186746  \n",
       "4   787.639514   22.882730     0.255519  \n",
       "5   778.352579   32.169665     0.557716  \n",
       "6   769.754381   40.767863     0.935728  \n",
       "7   754.819787   55.702456     1.851231  \n",
       "8   743.323572   67.198672     2.825128  \n",
       "9   726.675190   83.847054     4.971681  \n",
       "10  711.001395   99.520848     8.041916  \n",
       "11  698.351582  112.170662    13.602667  \n",
       "12  692.586293  117.935951    17.984872  \n",
       "13         NaN         NaN          NaN  \n",
       "\n",
       "[14 rows x 30 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"Case Studies/Case study 1.csv\")\n",
    "df_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[179, 183, 154, 178, 156, 191, 344, 177, 182, 342, 456, 341, 147, 155, 152]\n",
      "[0.21666666666666667, 0.10833333333333334, 0.075, 0.05, 0.05, 0.041666666666666664, 0.041666666666666664, 0.03333333333333333, 0.03333333333333333, 0.03333333333333333, 0.025, 0.025, 0.025, 0.025, 0.016666666666666666]\n",
      "[179, 183, 344, 191, 156, 153, 342, 155, 154, 181, 150, 5]\n",
      "[0.21666666666666667, 0.15, 0.06666666666666667, 0.058333333333333334, 0.058333333333333334, 0.05, 0.041666666666666664, 0.041666666666666664, 0.041666666666666664, 0.03333333333333333, 0.03333333333333333, 0.025]\n",
      "[179, 183, 191, 155, 153, 150, 156, 154]\n",
      "[0.275, 0.20833333333333334, 0.06666666666666667, 0.06666666666666667, 0.058333333333333334, 0.05, 0.041666666666666664, 0.03333333333333333]\n",
      "[183, 179, 154, 150, 153, 155, 191]\n",
      "[0.225, 0.15, 0.125, 0.11666666666666667, 0.075, 0.06666666666666667, 0.05]\n",
      "[179, 183, 151, 155, 181, 176, 452, 154]\n",
      "[0.25833333333333336, 0.21666666666666667, 0.08333333333333333, 0.06666666666666667, 0.058333333333333334, 0.058333333333333334, 0.05, 0.041666666666666664]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
      "  return bound(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "n_experiments = 5\n",
    "n_species = 5\n",
    "\n",
    "res_probilities, res_prediction, res_original_data, res_interpolated_data = prediction(df_raw, n_species, n_experiments, model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [0.21666666666666667, 0.10833333333333334, 0.075, 0.05, 0.05, 0.041666666666666664, 0.041666666666666664, 0.03333333333333333, 0.03333333333333333, 0.03333333333333333, 0.025, 0.025, 0.025, 0.025, 0.016666666666666666], 1: [0.21666666666666667, 0.15, 0.06666666666666667, 0.058333333333333334, 0.058333333333333334, 0.05, 0.041666666666666664, 0.041666666666666664, 0.041666666666666664, 0.03333333333333333, 0.03333333333333333, 0.025], 2: [0.275, 0.20833333333333334, 0.06666666666666667, 0.06666666666666667, 0.058333333333333334, 0.05, 0.041666666666666664, 0.03333333333333333], 3: [0.225, 0.15, 0.125, 0.11666666666666667, 0.075, 0.06666666666666667, 0.05], 4: [0.25833333333333336, 0.21666666666666667, 0.08333333333333333, 0.06666666666666667, 0.058333333333333334, 0.058333333333333334, 0.05, 0.041666666666666664]}\n"
     ]
    }
   ],
   "source": [
    "print(res_probilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [['SM + B + R -> P + B', 'P + B + R -> IMP2 + B'], ['SM + B + R -> P + B', 'SM + P + B -> IMP2 + B'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + B'], ['SM + B + R -> P + B', 'P + B -> IMP2 + B'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + IMP2 + B'], ['SM + B + R -> P + B + INT1', 'P + R + INT1 -> IMP2'], ['SM + R -> P', 'SM + P -> IMP2'], ['SM + B + R -> P + B', 'B + R -> IMP2 + B'], ['SM + B + R -> P + B', 'SM + B -> IMP2 + B'], ['SM + R -> P', 'P + R -> IMP2'], ['SM + C + B + R -> P + B', 'SM + P + B -> IMP2 + C + B'], ['SM + R -> P', 'P -> IMP2'], ['SM + B -> B + INT1', 'B + R + INT1 -> P + B'], ['SM + B + R -> B + INT1', 'B + R + INT1 -> P + B'], ['SM + B + R -> B + INT1', 'INT1 -> P + IMP2']], 1: [['SM + B + R -> P + B', 'P + B + R -> IMP2 + B'], ['SM + B + R -> P + B', 'SM + P + B -> IMP2 + B'], ['SM + R -> P', 'SM + P -> IMP2'], ['SM + B + R -> P + B + INT1', 'P + R + INT1 -> IMP2'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + IMP2 + B'], ['SM + B + R -> B + INT1', 'R + INT1 -> P + IMP2'], ['SM + R -> P', 'P + R -> IMP2'], ['SM + B + R -> B + INT1', 'B + R + INT1 -> P + B'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + B'], ['SM + B + R -> P + B', 'SM + P -> IMP2'], ['SM + B + R -> B + INT1', 'INT1 -> P'], ['SM + R -> INT1', 'INT1 -> P']], 2: [['SM + B + R -> P + B', 'P + B + R -> IMP2 + B'], ['SM + B + R -> P + B', 'SM + P + B -> IMP2 + B'], ['SM + B + R -> P + B + INT1', 'P + R + INT1 -> IMP2'], ['SM + B + R -> B + INT1', 'B + R + INT1 -> P + B'], ['SM + B + R -> B + INT1', 'R + INT1 -> P + IMP2'], ['SM + B + R -> B + INT1', 'INT1 -> P'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + IMP2 + B'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + B']], 3: [['SM + B + R -> P + B', 'SM + P + B -> IMP2 + B'], ['SM + B + R -> P + B', 'P + B + R -> IMP2 + B'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + B'], ['SM + B + R -> B + INT1', 'INT1 -> P'], ['SM + B + R -> B + INT1', 'R + INT1 -> P + IMP2'], ['SM + B + R -> B + INT1', 'B + R + INT1 -> P + B'], ['SM + B + R -> P + B + INT1', 'P + R + INT1 -> IMP2']], 4: [['SM + B + R -> P + B', 'P + B + R -> IMP2 + B'], ['SM + B + R -> P + B', 'SM + P + B -> IMP2 + B'], ['SM + B + R -> B + INT1', 'R + INT1 -> P'], ['SM + B + R -> B + INT1', 'B + R + INT1 -> P + B'], ['SM + B + R -> P + B', 'SM + P -> IMP2'], ['SM + B + R -> P + B', 'P + R -> IMP2'], ['SM + C + B + R -> P + B', 'P + B + R -> IMP2 + C + B'], ['SM + B + R -> B + INT1', 'B + INT1 -> P + B']]}\n"
     ]
    }
   ],
   "source": [
    "print(res_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment #</th>\n",
       "      <th>Mechanism</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P + B -&gt; IMP2 + B</td>\n",
       "      <td>0.108333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + B</td>\n",
       "      <td>0.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + B -&gt; IMP2 + B</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + IMP2 + B</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; P + B + INT1,  P + R + INT1 -&gt; IMP2</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + R -&gt; P,  SM + P -&gt; IMP2</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; P + B,  B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + B -&gt; IMP2 + B</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + C + B + R -&gt; P + B,  SM + P + B -&gt; IMP2 + C + B</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + R -&gt; P,  P -&gt; IMP2</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B -&gt; B + INT1,  B + R + INT1 -&gt; P + B</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + R + INT1 -&gt; P + B</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  INT1 -&gt; P + IMP2</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P + B -&gt; IMP2 + B</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + R -&gt; P,  SM + P -&gt; IMP2</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; P + B + INT1,  P + R + INT1 -&gt; IMP2</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + IMP2 + B</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  R + INT1 -&gt; P + IMP2</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + R + INT1 -&gt; P + B</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + B</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P -&gt; IMP2</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  INT1 -&gt; P</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + R -&gt; INT1,  INT1 -&gt; P</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P + B -&gt; IMP2 + B</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; P + B + INT1,  P + R + INT1 -&gt; IMP2</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + R + INT1 -&gt; P + B</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  R + INT1 -&gt; P + IMP2</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  INT1 -&gt; P</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + IMP2 + B</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + B</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P + B -&gt; IMP2 + B</td>\n",
       "      <td>0.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + B</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  INT1 -&gt; P</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  R + INT1 -&gt; P + IMP2</td>\n",
       "      <td>0.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + R + INT1 -&gt; P + B</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + B + R -&gt; P + B + INT1,  P + R + INT1 -&gt; IMP2</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P + B -&gt; IMP2 + B</td>\n",
       "      <td>0.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  R + INT1 -&gt; P</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + R + INT1 -&gt; P + B</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; P + B,  SM + P -&gt; IMP2</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; P + B,  P + R -&gt; IMP2</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + C + B + R -&gt; P + B,  P + B + R -&gt; IMP2 + C + B</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + B + R -&gt; B + INT1,  B + INT1 -&gt; P + B</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Experiment #                                             Mechanism  \\\n",
       "0              0           SM + B + R -> P + B,  P + B + R -> IMP2 + B   \n",
       "1              0          SM + B + R -> P + B,  SM + P + B -> IMP2 + B   \n",
       "2              0            SM + B + R -> B + INT1,  B + INT1 -> P + B   \n",
       "3              0               SM + B + R -> P + B,  P + B -> IMP2 + B   \n",
       "4              0     SM + B + R -> B + INT1,  B + INT1 -> P + IMP2 + B   \n",
       "5              0     SM + B + R -> P + B + INT1,  P + R + INT1 -> IMP2   \n",
       "6              0                          SM + R -> P,  SM + P -> IMP2   \n",
       "7              0               SM + B + R -> P + B,  B + R -> IMP2 + B   \n",
       "8              0              SM + B + R -> P + B,  SM + B -> IMP2 + B   \n",
       "9              0                           SM + R -> P,  P + R -> IMP2   \n",
       "10             0  SM + C + B + R -> P + B,  SM + P + B -> IMP2 + C + B   \n",
       "11             0                               SM + R -> P,  P -> IMP2   \n",
       "12             0            SM + B -> B + INT1,  B + R + INT1 -> P + B   \n",
       "13             0        SM + B + R -> B + INT1,  B + R + INT1 -> P + B   \n",
       "14             0             SM + B + R -> B + INT1,  INT1 -> P + IMP2   \n",
       "15             1           SM + B + R -> P + B,  P + B + R -> IMP2 + B   \n",
       "16             1          SM + B + R -> P + B,  SM + P + B -> IMP2 + B   \n",
       "17             1                          SM + R -> P,  SM + P -> IMP2   \n",
       "18             1     SM + B + R -> P + B + INT1,  P + R + INT1 -> IMP2   \n",
       "19             1     SM + B + R -> B + INT1,  B + INT1 -> P + IMP2 + B   \n",
       "20             1         SM + B + R -> B + INT1,  R + INT1 -> P + IMP2   \n",
       "21             1                           SM + R -> P,  P + R -> IMP2   \n",
       "22             1        SM + B + R -> B + INT1,  B + R + INT1 -> P + B   \n",
       "23             1            SM + B + R -> B + INT1,  B + INT1 -> P + B   \n",
       "24             1                  SM + B + R -> P + B,  SM + P -> IMP2   \n",
       "25             1                    SM + B + R -> B + INT1,  INT1 -> P   \n",
       "26             1                            SM + R -> INT1,  INT1 -> P   \n",
       "27             2           SM + B + R -> P + B,  P + B + R -> IMP2 + B   \n",
       "28             2          SM + B + R -> P + B,  SM + P + B -> IMP2 + B   \n",
       "29             2     SM + B + R -> P + B + INT1,  P + R + INT1 -> IMP2   \n",
       "30             2        SM + B + R -> B + INT1,  B + R + INT1 -> P + B   \n",
       "31             2         SM + B + R -> B + INT1,  R + INT1 -> P + IMP2   \n",
       "32             2                    SM + B + R -> B + INT1,  INT1 -> P   \n",
       "33             2     SM + B + R -> B + INT1,  B + INT1 -> P + IMP2 + B   \n",
       "34             2            SM + B + R -> B + INT1,  B + INT1 -> P + B   \n",
       "35             3          SM + B + R -> P + B,  SM + P + B -> IMP2 + B   \n",
       "36             3           SM + B + R -> P + B,  P + B + R -> IMP2 + B   \n",
       "37             3            SM + B + R -> B + INT1,  B + INT1 -> P + B   \n",
       "38             3                    SM + B + R -> B + INT1,  INT1 -> P   \n",
       "39             3         SM + B + R -> B + INT1,  R + INT1 -> P + IMP2   \n",
       "40             3        SM + B + R -> B + INT1,  B + R + INT1 -> P + B   \n",
       "41             3     SM + B + R -> P + B + INT1,  P + R + INT1 -> IMP2   \n",
       "42             4           SM + B + R -> P + B,  P + B + R -> IMP2 + B   \n",
       "43             4          SM + B + R -> P + B,  SM + P + B -> IMP2 + B   \n",
       "44             4                SM + B + R -> B + INT1,  R + INT1 -> P   \n",
       "45             4        SM + B + R -> B + INT1,  B + R + INT1 -> P + B   \n",
       "46             4                  SM + B + R -> P + B,  SM + P -> IMP2   \n",
       "47             4                   SM + B + R -> P + B,  P + R -> IMP2   \n",
       "48             4   SM + C + B + R -> P + B,  P + B + R -> IMP2 + C + B   \n",
       "49             4            SM + B + R -> B + INT1,  B + INT1 -> P + B   \n",
       "\n",
       "    Probability  \n",
       "0      0.216667  \n",
       "1      0.108333  \n",
       "2      0.075000  \n",
       "3      0.050000  \n",
       "4      0.050000  \n",
       "5      0.041667  \n",
       "6      0.041667  \n",
       "7      0.033333  \n",
       "8      0.033333  \n",
       "9      0.033333  \n",
       "10     0.025000  \n",
       "11     0.025000  \n",
       "12     0.025000  \n",
       "13     0.025000  \n",
       "14     0.016667  \n",
       "15     0.216667  \n",
       "16     0.150000  \n",
       "17     0.066667  \n",
       "18     0.058333  \n",
       "19     0.058333  \n",
       "20     0.050000  \n",
       "21     0.041667  \n",
       "22     0.041667  \n",
       "23     0.041667  \n",
       "24     0.033333  \n",
       "25     0.033333  \n",
       "26     0.025000  \n",
       "27     0.275000  \n",
       "28     0.208333  \n",
       "29     0.066667  \n",
       "30     0.066667  \n",
       "31     0.058333  \n",
       "32     0.050000  \n",
       "33     0.041667  \n",
       "34     0.033333  \n",
       "35     0.225000  \n",
       "36     0.150000  \n",
       "37     0.125000  \n",
       "38     0.116667  \n",
       "39     0.075000  \n",
       "40     0.066667  \n",
       "41     0.050000  \n",
       "42     0.258333  \n",
       "43     0.216667  \n",
       "44     0.083333  \n",
       "45     0.066667  \n",
       "46     0.058333  \n",
       "47     0.058333  \n",
       "48     0.050000  \n",
       "49     0.041667  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The true mechanism is SM + R + B -> P + B, P + R + B -> IMP2 + B\n",
    "\n",
    "data = {'Experiment #': [], 'Mechanism': [], 'Probability': []}\n",
    "\n",
    "# Iterate through res_prediction\n",
    "for i in res_prediction:\n",
    "    # Iterate through mechanisms and probabilities\n",
    "    for idx, mechanism_list in enumerate(res_prediction[i]):\n",
    "        probability = res_probilities[i][idx]\n",
    "        # Join the list elements into a single string\n",
    "        mechanism = ',  '.join(mechanism_list)\n",
    "        # Append data to lists\n",
    "        data['Experiment #'].append(i)\n",
    "        data['Mechanism'].append(mechanism)\n",
    "        data['Probability'].append(probability)\n",
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('res_case study 1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UeUgtz+bPn6/1eOTIkWjatClmz56N7du3Y+TIkXpfFxERgalTp2oeqwcbmVFkrcSUUTJBEARLd8IaZGVlQaFQwHvKVtSr7YY5A/0Q1MrL0t0iqjD1uZ2Zmck/ymUYNmwYjh8/juHDh6NNmza4e/culi5dipycHJw8eRKtWrUCACQnJ6Ndu3ZQKBSYPHkycnJysGjRIvj4+CAuLg6Ojo6afUZERGDBggUYN24cOnbsiF27dmHPnj3YvHlziR+s9GFGkbViRlkHZhRZK2aUdeD7SNaK57b5qRcbXbt2LUaPHl1m+8ePH6NatWoYM2YMVq9ebdDv4PtI1kqM57Y4KrVbmbuZeQjbeAYx8UpLd4WIKtHUqVNx69YtfPnll3j77bfxwQcf4OjRoygsLMSCBQs07ebNm4fc3FwcPHgQkydPxqxZs7B161acP38e69at07RLSUnB4sWLMXHiRKxcuRLjxo1DdHQ0unXrhunTp6OoqMiofjKjiEjMmFFERERkq1xcXODu7o60tDRLd4WI9OBAuhmop/hHRiegSMUJ/0S2okuXLlqzyQGgSZMmaNmyJS5duqTZ9uOPP2LAgAHw8fHRbOvduzeaNm2KrVu3arbt2rULBQUFmDBhgmabTCZDWFgYkpOTceLECaP6yYwiIjFjRhEREZGtys7ORmpqqk7JTyISB4MXGy3vFHqZTIbMzMxyd8haCACUmXmIS0xD58bulu4OkdUTa0YJgoB79+6hZcuWAIpnmd+/fx8dOnTQaRsQEIC9e/dqHp89exZVq1ZFixYtdNqpnw8MDNT7e59dyT0rK0u7X2BGEVUmsWaUWDGjiCoXM4qIrIVU8iwvLw8FBQU6ddk//vhjCIKAoKCgSu8TEZXN4IH0nJwcuLi4oE+fPqhZs6Y5+1QiMS/kV5L72Xkm2Q8RlU4MGaXPpk2bkJKSgo8++ggAoFQWlyrw8tKt/evl5YW0tDTk5+fDyckJSqUSderU0VlYQ/3aO3fulPh7S1rJ/VnHrz1AQCM3LuxHZGZizSix4+coosrBjCIiayGWPFu6dCkyMjI039mio6ORnJwMAJg0aRLS09PRrl07BAcHo3nz5gCAffv2Ye/evQgKCsLgwYMt1nciKpnBA+nBwcH4+eefERMTg6CgIISEhGDQoEFwdnY2Z/+0LFy4UO9Cfu3bt9dZyK979+5QKBSYN2+eZiG/ixcv6izkN3v2bJ2F/EJCQiCTycq1kF9JalevvP8+RLZMDBn1rL///hsTJ05E586d8eabbwIoXjwGAJycnHTaq/v6+PFjODk5af63tHYlKWkl92ctPXQdP55J4cJ+RGYmxoySgtTsfBSpBF7sIzIzZhQRWQux5NmiRYtw69YtzeMdO3Zgx44dAIDQ0FDUqFEDAwYMwP79+7F+/XoUFRXB19cX8+bNw7Rp0yCXsxIzkRgZ/C9z06ZNuHfvHtasWYPCwkKEhoaiTp06ePPNN7Fv3z6oVCpz9hOAdBbyAwAZAC+FMwIauVXgiInIUGLIqKfdvXsX/fv3h0KhwPbt22FnZwegePEYAFplV9Ty8vK02ri4uBjUTh8nJye4urpq/ZTYVy7sR2R2Yssoqfh4zyUELjzIfCIyM2YUEVkLseTZzZs3IQiC3p+GDRuiRo0a2LBhA65evYrc3Fzk5eUhPj4eERERcHBwqJQ+ElH5lesSV5UqVRASEoLdu3dDqVRi/vz5uHHjBvr16wcvLy9MmjQJly9fNldfJbOQn9qcgX6cQUVUiSydUWqZmZl4+eWXkZGRgZiYGNStW1fznLosi7rEy9OUSiXc3Nw0s9C9vLxw9+5dCIKg0w6A1n4rggv7EVUOsWSU1PBiH1HlYEYRkbVgnhGRuRh9r4i7uzsmTJiAo0eP4sqVK2jZsiWWLVuGH374wZT9K5N6Ib9atWoBKHshv7Nnz2oeG7KQX0ny8/ORlZWl9fO0aS81ZZkEIguyVEbl5eVh4MCBuHLlCnbv3g0/Pz+t5+vVqwcPDw+cOnVK57VxcXHw9/fXPPb398ejR4+0LhQCQGxsrOZ5U1Ev7LfueCIH04kqgVg+R0kBL/YRVT5mFBFZC+YZEZlShYou/fHHH5g0aRK6du2KI0eOoGvXrujZs6ep+mYQ9UJ+I0aMAGD4Qn7qthVZyE+hUGh+1LWHOzYsXsxi1e+J+O7ETZy4/pBf+ogspLIzqqioCCNGjMCJEyewbds2dO7cWW+7oUOHYvfu3UhKStJs++2333DlyhUMHz5cs23w4MFwcHDAsmXLNNsEQcA333yDevXqoUuXLiY/BpZRIKo8YvgcJRXqi31xiWmW7gqRzWBGEZG1YJ4RkakYvNio2sWLF/H9999jy5YtuHXrFtq0aYOpU6ciODhY70J25iTGhfyGtq+H878kIuNxAf676y8AxbXSuZAfUeWwZEb95z//wc8//4yBAwciLS0NGzdu1Ho+NDQUADBr1ixs27YNPXv2xHvvvYecnBxERUWhdevWGDNmjKZ9/fr1MWXKFERFRaGgoAAdO3bEzp07cfToUWzatElTd93U1GUUloe2Z24RmZiYPkdJ0fFrDxDQyI2l84jMhBlFRNaCeUZE5mDwQPq8efOwefNmJCQkoFGjRhg1ahRCQkJ0yhZUFjEs5KdvED5iRzxkTlW0tikz8/DOxjN4q2tD9Pbz5BdAIjMQQ0adO3cOABAdHY3o6Gid59UD6d7e3jhy5AimTp2KmTNnwtHREf3798fixYt1cmXBggWoWbMmVqxYgXXr1qFJkybYuHEjQkJCjOqjIckj/H+7yOgE9PHzZF4RmYAYMsoaLD10HT+eSeEEBSITY0YRkbVgnhGROcmEZ1exK4FcLoeLiwv69etXYrkCrR3LZAgPD69wB/XJzMzECy+8gNu3b+Po0aNagZiSkoL69etj4cKFeP/997Ve9/rrr2Pv3r14+PAhAGDcuHH4/vvvkZOTo1Xe5fr16/D19cWXX36JSZMmGdSnrKys4hIvU7ZC/sxA+rM4Q52kRH1uZ2ZmwtXV1dLdKZGYMkqM1O+jz/9nlKEFpza91Qldm9Qya9+IKoIZZR2ezqhnJyQ8S/2JjXfNkBQwo6yDVN5HovKyxnPbFvPMGt9HIkCc53a5BtLLtWOZDEVFRUZ1qjR5eXno27cvTp8+jQMHDugNxtq1a+OFF17A1q1btbY3a9YM9evXx2+//QYA+Prrr/Huu+/ir7/+0hqM//777zFq1Cj8/vvv6Natm0H9Ks9AOr8AkpSIMbj0EUtGiZX6fdx+4jIWHUqCMjPPoNfVcHHAgqGtmVUkWswo61Dei30yAJ4KZxyb0Yt3zZCoMaOsg1TeR6LyssZz2xbzzBrfRyJAnOe2waVdEhMTzdkPgzy9kN+uXbtKXchv/fr1SEpK0tS+Ui/k9/SVxsGDByM8PBzLli3D0qVLAZh/IT+AZROIzEEMGSUFffw8MSSgCdYdT8THey6V2T7jcQHrpROZgFgySr0mQ2xsLOLi4pCeno61a9di9OjRWu1Gjx6N9evX67y+WbNm+Pvvv7W2qVQqLFq0CMuXL4dSqUTTpk0RERGB4ODgcvdvyYi2Bl3se3rx0c6N3cv9e4hIm1gyioioophnRGROBg+kN2jQwJz9MIi1LOQH/PMF8OT1hyybQGQCYsgoqbCTyzC6ayOsPpaIu5l5BpV54YU/oooRS0alpqbio48+go+PD9q2bYvDhw+X2NbJyQmrV6/W2qZQKHTazZ49GwsWLMC4cePQsWNH7Nq1CyEhIZDJZBg5cmS5+qe+2Pf5/itYeuhame3vZpa8MDwRGU4sGUVEVFHMMyIyJ4MH0sXAWhbye9rE78+wbAIRVTo7uQxzBvohbOOZMtty5ieR9fDy8oJSqYSnpydOnTqFjh07ltjW3t5e89mqJCkpKVi8eDEmTpyoubvv7bffRo8ePTB9+nQMHz683BMT7OQydPWtZdBA+sd7LsHF0Y6fo4iIiIiIyOyMHkg/duwY1qxZgxs3biA9PR3PllqXyWQ4f/58hTv4tNJmTT2rZcuW2LdvX5nt5HI5IiIiEBERUYGeaZMBBi/kx7IJROZhiYySmqBWXlge2h4zf7yIjMcFZbY/fu0B7mfnoXZ1ZwQ0cuPsdKIKsFRGOTk5wdPT0+D2RUVFyM3NLbEm4a5du1BQUIAJEyZotslkMoSFhSEkJAQnTpxAYGBgufsZ0MgNXgrnMu+aSc99ws9RRGbAz1FEZC2YZ0RkSkYNpC9ZsgTTp0+Hs7MzmjVrBjc3N1P3S7IMre35LJZNIDIdZpThglp5obqzA0atji2z7dJD1zX/30vhjDkD/ThwRWQEqWTUo0eP4OrqikePHqFmzZoIDg7GwoULUa1aNU2bs2fPomrVqmjRooXWawMCAjTP6xtIz8/PR35+vuZxVlaW1vOG3jXDdWeITE8qGUVEVBbmGRGZmlED6VFRUejatSuio6P11sq0ZerannGJadifcBdrjt8s8zXqsgnrjieiVnUnzvYkqiBmVPk8/5y7QTM/n3Y3M4+zQImMJIWM8vLywvvvv4/27dtDpVIhJiYGy5Ytw/nz53H48GHY2xd/hFQqlahTpw5kMpnO6wHgzp07evc/f/58REZGltoH9V0zs366iLTcku+aYfkpItOSQkYRERmCeUZEpmbUQPqjR48watQoBlEJ7OQydG7sjs6N3RHQyM3gsgkf77mk+f+c7UlkPGZU+Tw989PQ0lScBUpkPClk1Pz587Uejxw5Ek2bNsXs2bOxfft2zSKijx8/1ll/BgCcnZ01z+sTERGBqVOnah5nZWXB29tbp11QKy88LlAh/IdzZfb5fnb57gYkIv2kkFFERIZgnhGRqcmNeVHPnj1x8eJFU/fFKgW18sLXo9qX+3Xq2Z4x8Uoz9IrIujGjyk8989NT4Wzwa56eBUpEhpNqRoWHh0Mul+PAgQOabS4uLlolWtTy8vI0z+vj5OQEV1dXrZ+SeLoalkup2fnYdS4FJ64/RJHK0PtriOhZlsyonJwczJkzB0FBQXBzc4NMJsO6dev0tr106RKCgoJQrVo1uLm54fXXX8eDBw902qlUKnz22Wdo1KgRnJ2d0aZNG2zevNnMR0JEYiDVz1xEJF5GDaR/9dVX+O2337Bo0SKkpXEApSzqsgnlma+p/voXGZ3AL4NE5cSMMk5QKy8cm9ELm8c9jy9G+uPdnr4GvY6zQInKR6oZ5eLiAnd3d60+e3l54e7duzoLdymVxRMB6tatW+Hfq154tLTPUXJZ8Z197205h+BVJxG48CAnIxAZyZIZlZqaio8++giXLl1C27ZtS2yXnJyM7t2749q1a5g3bx6mTZuGPXv2oE+fPnjy5IlW29mzZ2PGjBno06cPvvrqK/j4+CAkJARbtmwx9+EQkYVJ9TMXEYmXUQPp3t7e+Pe//42ZM2fCw8MDVatW1ZlVxFtn/qEumwCg3IPpnO1JVH7MKOOpS1MN9q+Hrr61DHrN1Xs5nAFKVA5Szajs7GykpqbCw8NDs83f3x+PHj3CpUuXtNrGxsZqnq8oQz5HPRs/vLOPyHiWzCgvLy8olUrcunULUVFRJbabN28ecnNzcfDgQUyePBmzZs3C1q1bcf78ea0Z7CkpKVi8eDEmTpyIlStXYty4cYiOjka3bt0wffp0FBUVmeU4iEgcpPqZi4jEy6ga6f/973/x6aefol69eujQoQODxwDqsgmR0QlQZpZv9iZnexKVDzPKNNSzQMtahHTpoWtYeuga13YgMpDYMyovLw8FBQWoXr261vaPP/4YgiAgKChIs23w4MEIDw/HsmXLsHTpUgCAIAj45ptvUK9ePXTp0sUkfSrv5yiu40BkPEtmlJOTEzw9Pcts9+OPP2LAgAHw8fHRbOvduzeaNm2KrVu3Yvz48QCAXbt2oaCgABMmTNC0k8lkCAsLQ0hICE6cOIHAwEDTHwgRiYLYP3MRkfQYNZD+zTffoH///ti5cyfkcqMmtdukoFZe6OPnibjENNzPzkNqdr7WAqMlUc/2DGjkxi+CRAZgRplGeRchVc8AXR7anoPpRKWwdEYtXboUGRkZuHPnDgAgOjoaycnJAIBJkyYhPT0d7dq1Q3BwMJo3bw4A2LdvH/bu3YugoCAMHjxYs6/69etjypQpiIqKQkFBATp27IidO3fi6NGj2LRpE+zs7EzW7/J+jnr6zr7Ojd1N1g8ia2fpjCpLSkoK7t+/jw4dOug8FxAQgL1792oenz17FlWrVkWLFi102qmf1zeQnp+fr7X+Q1ZWlqm6T0SVSOx5RkTSY9RA+pMnT9C/f38GkRHUZRMAoEglYPWxRM72JDIxZpTplGcWKGeAEhnG0hm1aNEi3Lp1S/N4x44d2LFjBwAgNDQUNWrUwIABA7B//36sX78eRUVF8PX11dQhfrbfCxYsQM2aNbFixQqsW7cOTZo0wcaNGxESEmLyvj/9OWrXuRSDXsM7+4jKx9IZVRb1GgxeXrrfiby8vJCWlob8/Hw4OTlBqVSiTp06kMlkOu0AaC4oPmv+/PmIjIw0cc+JqLKJPc+ISHqMSpMBAwbg6NGjpu6LzSlv7XTW+yQyDDPKtJ5ehHTiC8+V2pZrOxCVzdIZdfPmTQiCoPenYcOGqFGjBjZs2ICrV68iNzcXeXl5iI+PR0REBBwcHHT2J5fLERERgZs3byI/Px/x8fEYNWqU2Y+jdnVnk7YjomKWzqiyPH78GEBxGZhnOTs7a7V5/PixQe2eFRERgczMTM1PUlKSSfpORJVL7HlGRNJj1ED6nDlzkJCQgAkTJuD06dN48OAB0tLSdH6obOrZnp6Ksr/kqWetR0YncFE/olIwo0xPPQu0qaerQe05A5SoZMwo01Cv41DaZAS3qg64m5XHBZGJykHsGeXi4gIAWqVX1PLy8rTauLi4GNTuWU5OTjoLEhJJTZFKwInrD7HrXIrN/h0Ue54RkfQYVdqlWbNmAIBz585hxYoVJbbjKuiGebrm5/FrD7D00PUS27LeJ1HZmFHmwxmgRBXHjDINQ9ZxSMstQPgP5wCAJfKIDCT2jFKXZVGXeHmaUqmEm5ubZha6l5cXDh06BEEQtMq7qF9bt27dSugxUeWLiVfqlGa0xb+DYs8zIpIeowbS//vf/+rUmaOKUc/2NHQWJ2d7EpWMGWU+6hmgJa3tIAPgqXBGQCO3yu4akWQwo0ynPOs4cEFkIsOIPaPq1asHDw8PnDp1Sue5uLg4+Pv7ax77+/tj9erVuHTpEvz8/DTbY2NjNc8TWZuYeCXCNp7R+axui38HxZ5nRCQ9Rg2kz50718TdIDXO9iSqOGaU+ZQ1A1QAMGegHxcaJSoFM8q0nr6z78ytNET9ekVvOy6ITGQYKWTU0KFDsX79eiQlJcHb2xsA8Ntvv+HKlSsIDw/XtBs8eDDCw8OxbNkyLF26FAAgCAK++eYb1KtXD126dLFI/4nMpUglIDI6Qe+EF1v8OyiFPCMiaTFqIJ3Mh7M9iUjsSpsBWt3ZHh680EdElUx9Z19ZWCKPSPyWLl2KjIwM3LlzBwAQHR2N5ORkAMCkSZOgUCgwa9YsbNu2DT179sR7772HnJwcREVFoXXr1hgzZoxmX/Xr18eUKVMQFRWFgoICdOzYETt37sTRo0exadMm2NnZWeQYicwlLjGt1Du0+HeQiKhiDFpsdMeOHZoPL+VRUFCAHTt2IDU1tdyvtVXq2Z4A9C6eJQCY1a+5ztVjLiRCtowZVfmCWnnh2Ixe2DzueXwx0h9Lg9vBz8sV2XmFCF51EjvPpmi1Z0aRLWNGVR6WyCMqP7Fl1KJFi/Dhhx9i+fLlmv59+OGH+PDDD5Geng4A8Pb2xpEjR9C4cWPMnDkTn332Gfr164f9+/dr6qOrLViwAPPmzcO+ffswceJE3Lx5Exs3bkRISIhJ+00kBrb+d1BseUZE1seggfThw4fj999/L/fOs7KyMHz4cFy4cKHcr7Vl6tmengr9szpXHU1EcvojzeOYeCUCFx5E8KqTeG/LOQSvOonAhQcRE6+7AA+RNWJGWYZ6Buhg/3oY0LYutr3TGX396uBJoQpTfjiHxb9ehkolMKPI5jGjKg9L5BGVn9gy6ubNmxAEQe9Pw4YNNe1atmyJffv2ITc3F+np6di4cSPq1Kmjsz+5XI6IiAjcvHkT+fn5iI+Px6hRo0zaZyKxsPW/g2LLMyKyPgaVdhEEAUePHkVhYWG5dp6Tk2NUp0i73uf97DzUru6M/MIihP9wDheSMzHwq2P4MrgdcvMLuZAI2TxmlDhUdbLHN6H/wmf7LuObI9fx1cFrOH4tFWduZ+i0ZUaRLWFGVR6WyCMqP2YUkfWw9b+DzDMiMjeZIAhl3l8vlxs0cb1EBw4cQK9evSq0D7HLysqCQqFAZmYmXF1dzfZ7ktMfIWzjGVxMyYRcBlR1tEd2vv4/Euo/ksdm9LKJhUTIPCrr3K4IZlTZKvt93HYqCbN+uoiCopL/xDCjyBSYUdbBlO9jTLxS7yQDoDh3eAGPKhMzyjpI4X0kUlP/HQSg9bdQ/Wn76b+D1nZu22qeWdv7SKQmxnPboBnpiYmJFfolnp6eFXo9/aN+zSrY9k5nzNn1F344lVTiIDrAhUTIdjCjxGd4B2/k5BUicndCiW2YUWQrmFGVq7QFkf/d4zkOohM9gxlFZF1K+jvoqXDGnIF+Vv13kHlGROZm0EB6gwYNzN0PKgdnBzssHNYGkAE//JlUZntrXUiESI0ZJU5u1RwNaseMImvHjKp8T5fIU2Y+xg9/JiE2MQ2rjyaidb0a6N/GegcRiMqLGUVkffSVig1o5Gb1d4Eyz4jI3AwaSCdxGuJfz6CBdGtdSISIxM3WFzsiIstSL4gMAIPa1sX07Rfw09kUTNp8Bk+K2uKVdvUt3EMiIiLzefrvIBERmUbFCkiRRakXEimJDICXFS8kQkTips6okua9MKOIqLLY28mxaHhbjOjgDZUATN16HlufmoxQpBJw4vpD7DqXghPXH6JIVeYSQkREREREZGM4I13C7OQyzBnoV+KCWgAwZ6Cf1d++RUTi9HRGyQCdnBLAjCKiymMnl2H+q63haC/HhpO38P6PF5BfpIJHNUedOrJeNlBHloiIyq9IJdhcuRQiIvoHB9IlrrQFtUZ09OYXQCKyqNIyCoDebfyCQkTmIpfL8NHglnC0l+PbY4n4cGe83nZ3M/MQtvEMloe252cpIiICAMTEK3nhlYjIxnEg3Qo8vZDIvazH+O3SfURfUGLLn0lo7lkdo7s20mrPQSoiqkzPLnbkUc0JR648wIrfbyAyOgGFRQLGdX8OAL+gEJH5yWQyfNC/BRzt5Fh+5LreNgKKy09FRiegj58nPycREdm4mHil3jvBeeGV9MnJyUFUVBRiY2MRFxeH9PR0rF27FqNHj9Zpe+nSJYSHh+PYsWNwdHRE//79sWTJEnh4eFR+x4moTBxItxJPLyQy2L8e6tWsgm+OXMfc6AQIAMb8/2A6B6mIyBKeXeyoc2N3ONrL8dXBa/h07yUUqFR4rlZVfkEhokohk8nQrUmtEgfSgeLBdGVmHuIS07hYGxGRDStSCYj8/+/Vz+KFV9InNTUVH330EXx8fNC2bVscPnxYb7vk5GR0794dCoUC8+bNQ05ODhYtWoSLFy8iLi4Ojo6OldtxIiqT0QPp+/btw7fffosbN24gPT0dgqD9Z0Umk+H69ZK/nJD5yGQyzAhqBrkMWHb4evEffQGoW8OZg1RkM5hR4iaTyfCfvs1gL5fj8wNX8FnMZVRzsucXFLIZzCjLe5CTb1C7+9m6JaiIrB0ziugfcYlpessRqvHCq7hZIs+8vLygVCrh6emJU6dOoWPHjnrbzZs3D7m5uTh9+jR8fHwAAAEBAejTpw/WrVuH8ePHm7RfRFRxRg2kR0VFYebMmahTpw4CAgLQunVrU/eLKkgmk2H6S80gkwFfH7qOj3YnoLozB6nINjCjpOO93k1gbydD1L7LyMkvLLEdv6CQNWFGiUPt6s4mbUdkLZhRRNoMvaDKC6/iY6k8c3JygqenZ5ntfvzxRwwYMEAziA4AvXv3RtOmTbF161YOpBOJkFED6V988QV69eqFvXv3wsHBwdR9IhORyWSY1rcZ5DIZvjp4Ddl5HKQi28CMkpaJPX1x+W42fj5/p8y2/IJC1oAZJQ4BjdzgpXDG3cw8vRMNZAA8FcVryRDZEmYUkTZeeJUuMedZSkoK7t+/jw4dOug8FxAQgL1795b42vz8fOTn/3NnXVZWlln6SES65Ma8KD09HcOGDRNdEJEumUyGqX2a4qWWdQxqz0EqsgaWzKicnBzMmTMHQUFBcHNzg0wmw7p163TajR49GjKZTOenefPmOm1VKhU+++wzNGrUCM7OzmjTpg02b95cCUdTeYIDfMpuBH5BIevAz1HiYCeXYc5APwDFg+bPEgDMGejHO/XI5jCjiLSpL7yW9NdAhuJ1x3jhVXzEnGdKpRJAcRmYZ3l5eSEtLU1rsPxp8+fPh0Kh0Px4e3ubta9E9A+jBtIDAgJw+fJlU/eFzEQmk2F0l0YGteUgFVkDS2aUemGZS5cuoW3btqW2dXJywoYNG7R+oqKidNrNnj0bM2bMQJ8+ffDVV1/Bx8cHISEh2LJli7kOo9Kpv6CUhF9QyJrwc5R4BLXywvLQ9vDUkz8OdjLUquZkgV4RWRYzikhbaRde1Y954VWcxJxnjx8/BlD8nfBZzs7OWm2eFRERgczMTM1PUlKS+TpKRFqMKu2ybNkyvPzyy+jQoQNCQkJM3ScyA/UgVUmLpPD2ZbImlswoQxeWAQB7e3uEhoaWur+UlBQsXrwYEydOxNKlSwEAb7/9Nnr06IHp06dj+PDhsLOzM+kxWIL6C4q+BZHV+AWFrAU/R4lLUCsv9PHzRFxiGu5n58G9qhPWHE/Ewb/vY+y6P7E9rAua1qmuaV+kEjRta1cv/uzEbCJrwowi0qW+8BoZnaD1ndpT4Yw5A/0Q1Ep3VjFZnpjzzMXFBQD0zjrPy8vTavMsJycnvQPwRGR+Rg2kjxgxAoWFhXj99dcRFhaG+vXr6wzkyGQynD9/3iSdpIrjIBXZEktmlKELy6gVFRUhNzcXrq6uep/ftWsXCgoKMGHCBM02mUyGsLAwhISE4MSJEwgMDKxwv8WgpC8oAODvXQNt6tewTMeITIyfo8THTi7TWiPmXw1qYtTqkzhzOwNvfBuHHyd0Qb0aLoiJV+pklBcHUcjKMKOI9Hv2wisvpoqfmPNMXdJFXeLlaUqlEm5ubhwsJxIhowbS3dzc4O7ujiZNmpi6P2RGpQ1SdfF1R8eGnI1O1kEqGfXo0SO4urri0aNHqFmzJoKDg7Fw4UJUq1ZN0+bs2bOoWrUqWrRoofXagIAAzfP6BtKlugDN019Qbqfl4vcrqYj56y7OJmXgxcVH8F7vJngrsBEc7IyqTEYkClLJKFvm4miHNaM7Ytg3J3Dtfg7e+DYWYS80xvRtF3QmJNzNzEPYxjNYHtqeg+lkFZhRRCV79sIriZuY86xevXrw8PDAqVOndJ6Li4uDv79/5XeKiMpk1ED64cOHTdwNw+Xk5CAqKgqxsbGIi4tDeno61q5di9GjR2u1Gz16NNavX6/z+mbNmuHvv//W2qZSqbBo0SIsX74cSqUSTZs2RUREBIKDg815KBbx9CDVtfvZ2J9wD79fTcXxaw/Rc9FhTHupGUZ1asCr6iRplswoQ3l5eeH9999H+/btoVKpEBMTg2XLluH8+fM4fPgw7O2L41mpVKJOnTqQyWQ6rweAO3fu6N3//PnzERkZad6DMBP1F5TOjd0xoqMP/r6bhQ9+isepW+lY8Mvf+PF0Mj4Z0gqdnuOXGJImKWQUATWqOOK7sQEYuvwPXH+Qi4gdF/Xe1SeguEReZHQC+vh58jMUSR4zioishdjzbOjQoVi/fj2SkpI0C4b+9ttvuHLlCsLDwy3cOyLSx6iBdEtSL+Tn4+ODtm3blhqMTk5OWL16tdY2hUKh02727NlYsGABxo0bh44dO2LXrl0ICQmBTCbDyJEjTX0IFvf0INXrnRvi1M00/HfXX0hQZuG/u/7ClrgkfDS4JTpwhjqR2cyfP1/r8ciRI9G0aVPMnj0b27dv12TP48ePjV6AZurUqZrHWVlZkl3NvbmnK7b+uzN+PJOM+b/8jav3czBi5Um82r4eIl5uAY/qvOWRiMyjbg0XfDc2AEOWHUduflGJ7QQAysw8xCWmcaYiERERYenSpcjIyNBMfIqOjkZycjIAYNKkSVAoFJg1axa2bduGnj174r333tNMHG3dujXGjBljye4TUQmMHkgvKirCxo0bsWfPHty6dQsA0KBBAwwYMACjRo0y2+J3XMjP9Do0dEP0pEB8H3sLUfsuI0GZhWHfnMCr7eth5svNUbu6s6W7SFRulsqoiggPD8eHH36IAwcOaAbSXVxcuAANALlchuEdvNHHrw4+23cZm+NuY8eZFBxIuIfpQc0REuDDWaAkKVLMKFvVpE51jOv2HP534GqZbe9n61/UnUhqmFFEZC0slWeLFi3S/D4A2LFjB3bs2AEACA0NhUKhgLe3N44cOYKpU6di5syZcHR0RP/+/bF48WKr+i5HZE2MKjKbmZmJrl27YuzYsfj1119RUFCAgoIC7N+/H2PGjEFgYKDZ6vEas5BfaX0pbSG/5ORknDhxokL9lQo7uQyvd26IQ9NeQHCAN2QyYMeZFPRadASrj95AQZHK0l0kMpglM6oiXFxc4O7ujrS0NM02Ly8v3L17F4KgXVBAvShN3bp1K7WPllajiiPmvdIaO8K6oGVdV2TlFeLDnfF4ZdlxXEjOsHT3iAwi1YyyZZ0aGTbLnJMPyBowo4jIWlgyz27evAlBEPT+NGzYUNOuZcuW2LdvH3Jzc5Geno6NGzeiTp06ZukTEVWcUQPps2fPxunTp/HVV1/hwYMHOHPmDM6cOYP79+9j6dKlOHXqFGbPnm3qvpabeiE/hUIBNzc3TJw4ETk5OVptDFnIT5/8/HxkZWVp/VgD92pOmP9qG/w0oSva1lcgJ78Qn+y5hP5fHsWJ6w8t3T0ig0glo56VnZ2N1NRUeHh4aLb5+/vj0aNHuHTpklbb2NhYzfO2qJ1PTfz8biAiB7VEdSd7XEjOxOCvj+PDnfHIfFRg6e4RlUqqGWXLAhq5wUtR8iC5DICXwhkBjVgWj6SPGUVE1oJ5RkSmZtRA+k8//YQJEyZgwoQJcHBw0Gx3cHBAWFgYwsLC8OOPP5qsk8ZQL+S3du1abN68GYMGDcKyZcsQFBSEwsJCTbuKLOSnUCg0P1KtPVwSf+8a+GlCVyx4tTVqVnHAlXs5CF51EpM2n4UyU39N5iKVgBPXH2LXuRScuP4QRSp9S3IRmZ/YMyovLw/Z2dk62z/++GMIgoCgoCDNtsGDB8PBwQHLli3TbBMEAd988w3q1auHLl26VEqfxchOLsObXRrit2k9MMS/LgQB2HDyFl5cchg7ziTrzOJnRpFYiD2jSJedXIY5A/1QWgGpOQP9WGKKrAIzioisBfOMiEzNqBrpDx8+RLNmzUp8vnnz5lqlCSyBC/lVnFwuw8gAHwS18sTiX69gU+wtRJ+/g98u3cPkF5tgbNdGcLQvvhYTE69EZHQClJn/1Ab1UjhjzkA/BLXystQhkI2ydEaVtbBMeno62rVrh+DgYDRv3hwAsG/fPuzduxdBQUEYPHiwZl/169fHlClTEBUVhYKCAnTs2BE7d+7E0aNHsWnTJtYoRXEphf+NbIfXOnrjw53xuP4gF1O3nseWP5PwyZBWaFqnOjOKRMXSGUXGCWrlheWh7TH3579wN+uftStkMmB05wZ4qaXhpQeJxIwZRUTWgnlGRKYmE56dsmeAVq1aoX79+oiJidH7fFBQEJKSkvDXX39VuIOlUS82unbtWowePbrM9o8fP0a1atUwZswYrF69GgAwYMAAXLp0CdevX9dq++jRI1StWhUzZ87UGZTXJysrCwqFApmZmXB1dTXqeMQuPiUT/90VjzO3MwAAz3lUReSglsjNL0TYxjN49kRSz8laHtqeA1USJsVz29IZ1bBhQ62FZZ6WmJiIGjVqYNKkSTh58iTu3LmDoqIi+Pr6YtSoUZg2bZrWbAkAUKlUWLhwIVasWAGlUokmTZogIiICo0aNMrhPUnwfjfGkUIXVx27gy9+uIq9ABXu5DL2a18avCfd02jKjrIMUz21LZ5QYSel9LFIJiEtMw9mkdGw7lYzE1FwAwIvNa2Peq61Rx5V10ukfUjq31ZhRuqT4PkqFOlPvZ+ehdvXiElm8u6fyWPu5bSt5Zu3vI9kuMZ7bRs1InzBhAt59913069cPU6ZMQdOmTQEAly9fxpdffon9+/dj6dKlJu2oKZS0kN+hQ4cgCIJWeRdbXcivNK3qKbD9nS746WwK5v/yN248yMXr38bB2V6uM4gOAAKKB6oioxPQx8+TH4io0lg6o27evFlmmw0bNhi8P7lcjoiICERERFSgV7bB0V6OCS/4YlDbuoiMTsD+hHt6B9EBZhRZjqUziirGTi5D58bu6NzYHeO6PYeVv9/AFweu4re/76PPkiP4cIAfhv2rvk7ZQCKpYEZRZeEdg2RuzDMiMjWjB9Lv37+PBQsWYN++fVrPOTg44L///S/CwsJM0kFTKmkhv9WrV+PSpUvw8/PTbLf1hfxKIpfLMPRf9dGnZR38b/9VrPsjEXmFqhLbCwCUmXmIS0xD58bulddRsmlSzSgynfo1q2DVGx3w9cFriPr1contmFFkCcwo6+FgJ8fEnr7o41cH07edx/nkTEzffgF7Liox75XWqFvDxdJdJCo3ZhRVhph4pd67mu9m5iFs4xneMUgmwTwjIlMzqrSLWmpqKg4cOKApYdCgQQP07t0btWrVMlkHS1NSaZe8vDwUFBSgevXqWu3ff/99REVFYceOHXjllVcAAMnJyXjuuecwfvx4zZVIQRDQo0cP3LhxA7du3TKoBrEYbzeoDMsPX8PCmJIHqdS+GOmPwf71KqFHZGpSPrctnVFiIuX3sSJ2nUvBe1vOldmOGSVdUj63mVH/kPL7qFZYpMLqY4lYsv8KnhSqUN3JHrP7t8CIjt6cnW7DpHxuM6P+IeX3UYyKVAICFx7Umon+NBkAT4Uzjs3oxTsGzcxWzm1rzzNbeR/J9ojx3DZqRrparVq1NIt2ViYu5Cce/t41DWpXuzrrhVLls1RGkXgYmj3MKLIEZpR1sbeT450ejdG7RR1M334eZ29nYOaOi9hzUYn5r7ZG/ZpVLN1FonJhRpG5xCWmlTiIDvCOQTI95hkRmYpBA+m3b98GAPj4+Gg9Lou6vaktWrRIayG/HTt2YMeOHQCA0NBQ1KhRAwMGDMD+/fuxfv16zUJ+8+bNw7Rp0yCXy7X2t2DBAtSsWRMrVqzAunXr0KRJE2zcuBEhISFm6b81CWjkBi+FM+5m5umtkw4U17kLaORWqf0i2yK2jCLxMCSjqjnZoa23olL7RbaFGWVbfGtXw/Z3umDt8URE7buMo1dT8dLnvyOiXwuEBPhAztmVJDLMKKpMBUUqfHfipkFt72eXPNhOpA/zjIjMzaDSLnK5HDKZDI8fP4ajo6PmcVmKiopM0kkpEOPtBpVFXd8OgN6Bqk6N3LDi9X+hRhXHyu0YmYQUzm1mVNmk8D6aS1kZBQBN61TDwqFt0M7HsLtsSDykcG4zo8omhffRGDce5OD97Rdw6lY6AKBLY3csHNoG3m6cnW4rpHBuM6PKJoX3UQou383Gf7adQ3xKlkHtN497njPSzczazm1bzTNrex+J1MR4bhs0I33NmjWQyWRwcHDQekwEAEGtvLA8tL3OiutVHe3wqKAIsYlp6L3kd3z6Siu81NLTgj0la8WMotKUlFGerk4Y2LYufjqbgiv3cvDq8j8wtmsj/KdvU1RxrFDlMyItzCjb9ZxHNfzw785Y/8dNfLbvb/xx/SFe+t/vmPlyc4R2asDZ6SQKzCgyt8IiFVb8fgNfHLiKJ0UqKFwcIAOQ8bhAb3t1jXTe1UzlxTwjInOr0GKj9A8xXiWpbEUqAXGJabifnYfa1Ys/+FxIzsD07Rdw7X4OAGBQ27qIHNQSNatydrpU8Ny2Dnwf9WeUnVyG9Nwn+Hh3AnacTQEA+LhVwYJXW6OLr3UsPmTteG5bB1t4H2+m5uL9Hy8gLjENQPEde58Na4MG7lUBlJxRJG22cG7bAr6Pxrt2Pxv/2XYB55MyAAAvNq+N+a+2xpnb6XrvGFSn3vLQ9ghq5VWpfbVFPLetA99HslZiPLflZTfRNXbsWMTGxpb4fFxcHMaOHWt0p0ia7OQydG7sjsH+9dC5sTvs5DK086mJ3ZMCEfZCY8hlwM/n76DP50cQE6+0dHfJijGjSB99GQUANas6YskIf6wd0xF1Fc64nfYIIatjMfPHC8gsYaYUUUUwo2xTw1pVsWXc8/hocEtUcbRDbGIaXvrf71hzLBF7L95B4MKDCF51Eu9tOYfgVScRuPAgPy+RRTCjyBSKVAJW/X4D/b48hvNJGajubI/Fw9ti9ZsdUNvVWXPHoKdCe8F3T4UzB9HJZJhnRGRqRg2kr1u3DtevXy/x+cTERKxfv97oTpF1cXaww4yg5vhpQlc0rVMNqTlP8M7GM3j3+zN4mJNv6e6RFWJGkTF6NquNX6f2wBudGwAAtvyZhD5LjuDXv+5auGdkbZhRtksul+GNzg2xb0p3dH7OHXkFKny0OwETNp3VKj0FAHcz8xC28QwH06nSMaOoohJTc/HaihP4dO8lPClUoUdTD/wa3h1D/1Vfq8xGUCsvHJvRC5vHPY8vRvpj87jncWxGLw6ik8kwz4jI1IwaSC/LnTt34OLiYo5dk4S19a6B6EmBeLenL+zkMuy+oETfz3/H3ov8gkiVixlFJanmZI+PBrfC1n93xnO1quJ+dj7GbziNid+fwYNs/Rf+ilQCTlx/iF3nUnDi+kMUqVgxjSqGGWX9vN2qYNPbnfDx4JYoqXiLOkkioxOYKyQqzCgqiUolYM2xRLz8xe84fSsd1ZzssXBoa6wb0xFeCv3nTEl3DBJVBuYZEZWXwaup7dq1C7t27dI8XrlyJQ4cOKDTLiMjAwcOHEDHjh1N00OyKk72dpj2UjO81NIT07adx+V72Ziw6Qz6t/ZC5OCWqFXNydJdJIliRpEpBTRyw973uuGL365i5e83sOeCEsevpeK/A/zwSrt6mtlUMfFKnUVMvRTOmDPQj7OpSAszip4ll8vgW7s6ShsiFwAoM/MQl5iGzo3dK6trZIOYUVRRtx8+wrTt5zXrQHT1dcfCoW1Qv2YVC/eMbA3zjIjMyeCB9ISEBGzbtg0AIJPJEBsbi9OnT2u1kclkqFq1Krp3744lS5aYtqdkVVrXVyB6UiCWHryKrw9fx56LSpy48RAfDW6J/q29uLI2lRszikxNXZaqf2svvL/9AhKUWZi69Tx+Pn8Hn77SGheTMxC28YzOIJi6HAPre9LTmFGkz/3svLIblaMdkbGYUWQslUrAprjbmL/3Eh49KUIVRzvM6tcCozr58DsdWQTzjIjMSSYIQrnvFZXL5di4cSNCQkLM0SdJEuNKslIRn5KJadvO4++72QCAl1t54qPBreBRvXh2epFKQFxiGu5n56F2dWcENHLjLX+VSIrnNjNKlxTfRzEpKFJh5e838MVvV/GkUIWqjnawk8uQlVeot70MxYtlHZvRi3llZlI8ty2ZUTk5OYiKikJsbCzi4uKQnp6OtWvXYvTo0TptL126hPDwcBw7dgyOjo7o378/lixZAg8PD612KpUKixYtwvLly6FUKtG0aVNEREQgODjY4H5J8X00hRPXHyJ41cky220e9zxnpEuUFM9tfo7SJcX30dT0fSdTZj7GjB8v4Pi1hwCATo3cEDWsLXzcOQtdKqz93LaVPLP295FslxjPbYNnpD9NpVKZuh9kw1rVU+DndwPx9aFr+PrQNfwSfxcnbzzE3EEt4Wgnw0e7L7FsApULM4pMzcFOjok9ffFSS0/M+PECTt9KL7U9yzFQaSyZUampqfjoo4/g4+ODtm3b4vDhw3rbJScno3v37lAoFJg3bx5ycnKwaNEiXLx4EXFxcXB0dNS0nT17NhYsWIBx48ahY8eO2LVrF0JCQiCTyTBy5MhKOjJpCmjkBi+FM+5m5pVY4kUG4PLdLHRq5AY5L8xRJeDnKHqWvlJ2Chd75BeqkFeggrODHDOCmuPNzg2ZUyQqzDMiMjWzLDZKVF6O9nKE92mKXe92RQsvV6Q/KsB7W84hbNNZrQ9swD9lE2LiuUgpEVUu39rVsO3fnTG0fT2D2rMcA4mNl5cXlEolbt26haioqBLbzZs3D7m5uTh48CAmT56MWbNmYevWrTh//jzWrVunaZeSkoLFixdj4sSJWLlyJcaNG4fo6Gh069YN06dPR1FRUSUclXTZyWWYM9APAEpddHRudAJCv41FUtqjSusbERFQPIgetvGMzneyzMeFyCtQ4TmPqvjlve4Y07URB9GJiMhgRSoBJ64/xK5zKThx/SGKVOUumGIRRg+k//LLL+jTpw/c3d1hb28POzs7nR+i8mpZV4Gf3+2KKS82KbGN+p9WZHSCZP6hUeVjRpG5yOUyDPuXt0Fta1d3NnNvSKoslVFOTk7w9PQss92PP/6IAQMGwMfHR7Otd+/eaNq0KbZu3arZtmvXLhQUFGDChAmabTKZDGFhYUhOTsaJEydMewBWKKiVF5aHtoenQjsvvBTOWDaqHSIHtYSLgx3+uP4QQf/7Hd/H3oYRlRmJyoWfowgoHuSIjE4odVHkR/mF8HFjKRcSL+YZkfjExCsRuPAggledxHtbziF41UkELjwoiQmzRg2kq79c3bt3DyNHjoRKpUJwcDBGjhwJFxcXtGnTBv/9739N3VeyEQ52cnR6rvRSCE+XTSB6FjOKzE1djqEkMhQPggU0cqu8TpFkiD2jUlJScP/+fXTo0EHnuYCAAJw9e1bz+OzZs6hatSpatGih0079vD75+fnIysrS+rFlQa28cGxGL2we9zy+GOmPzeOex7EZvdCvdV282aUhfnmvGzo2rIncJ0WY9dNFvLEmDncyHlu622SlxJ5RVHniEtN0ZqI/625WPr+TkWgxz4jEp6Q7naRSfcKogfT58+drvkhFRkYCAMaOHYtNmzYhPj4eSqUSjRo1MmlHybYYWg6BZRNIH2YUmZu6HIMMuuUY1I/nDPTjQqOkl9gzSqks/vDq5aW7FomXlxfS0tKQn5+vaVunTh3IZDKddgBw584dvb9j/vz5UCgUmh9vb8Pu8rBmdnIZOjd2x2D/eujc2F0rPxrWqoot4zvjg/4t4GQvx9GrqXjp89+x9VQSZ6eTyYk9o6jy3Msy7IIdv5ORWDHPiMSltDudpFJ9wqiB9ISEBIwcORJ2dnawty9er7SgoAAA0LBhQ0yYMAELFy40XS/J5hhaDoFlE0gfZhRVhpLKMXgqnLE8tD0XRKYSiT2jHj8uHjhxcnLSec7Z2VmrzePHjw1q96yIiAhkZmZqfpKSkkzSd2tmJ5fh7W7PYe973dDOpway8wvx/vYLeGv9KdzL4iAWmY7YM4oqx52Mx1hz/KZBbfmdjMSKeUYkLmXd6SSF6hNGDaRXqVIFjo6OAIAaNWrAyclJM3sJAOrUqYPExETT9JBskrpsQklzOVk2gUrDjKLKUlI5Bg6iU2nEnlEuLi4AoJl1/rS8vDytNi4uLga1e5aTkxNcXV21fsgwjT2qYfs7XTDz5eZwtJPj4N/30WfJEfx0Npmz08kkxJ5RAHD48GHIZDK9PydPntRq+8cffyAwMBBVqlSBp6cnJk+ejJycHAv1XPwEQcDmuNvo+/nvuJCcWWpbficjsZNCnhHZEmuoPmHUQHqzZs2QkJCgeezv748NGzagsLAQeXl5+P7777UWpyIqL3XZBIBlE6j8mFFUmUorx0Ckj9gzSl2W5ekvmmpKpRJubm6aWeheXl64e/euzgCu+rV169Y1c29tk51chnd6NMbuyYFoXU+BrLxChP9wHv/ecBoPsrUvbBSpBJy4/hC7zqXgxPWHor5VlsRB7Bn1tMmTJ2PDhg1aP76+vprnz507hxdffBGPHj3CkiVL8Pbbb2PlypUYPny4BXstXklpj/D6t3GI2HEROfmF+FeDmpjLUnYkYVLKMyJbYA3VJ+yNedErr7yCL7/8EosWLYKTkxNmz56NwYMHo0aNGpDJZMjNzcWaNWtM3VeyMeqyCZHRCVq3fngqnDFnoB9nfFKJmFFEJGZiz6h69erBw8MDp06d0nkuLi4O/v7+msf+/v5YvXo1Ll26BD8/P8322NhYzfNkPk3rVMeOCV3wzeHr+PLgVfyacA9/3kzDR4NbYWDbuoiJV+p8jvLi5ygqg9gz6mndunXDsGHDSnx+1qxZqFmzJg4fPqy586Vhw4YYN24cfv31V/Tt27eyuipqKpWATXG3sWDvJeQ+KYKzgxzT+jbDmK6NYCeXwVPhzO9kJElSyjMiW6CuPnE3M09vnXQZiv++iPlOJ5lgontAjx49ih07dsDOzg79+/dHz549TbFbycjKyoJCoUBmZiZvTzaxIpWAuMQ03M/OQ+3qxf+gOOuh8ljLuc2Mso73kehZ1nJuWyKjTp06hY4dO2Lt2rUYPXq01nNhYWFYv349Ll++rFkI9LfffkPv3r2xfPlyvPPOOwCA5ORkPPfccxg/fjyWLl0KoLgsQI8ePXDjxg3cunULdnZ2ZfbFWt5HS0q4k4Vp284jQZkFAGjvUwNnbmfotFN/guJaDpXDWs5tsX2OOnz4MHr27Ilt27bhpZdegouLi6b+sVpWVhbc3d0RHh6Ozz77TLP9yZMncHd3x4gRI7B69WqDfp+1vI/63H74CO//eB4nbxTXo+3YsCY+G9YWjWpV1WrH72TWyZrP7ZKILc9MwRbfR5KumHglwjaeAQCtwXR9n1HFeG6XeyA9Pz8f+/btQ8OGDdGmTRtz9UtyxPjmEpmC1M5tZpR+UnsfiQwltXNbDBm1dOlSZGRk4M6dO1i+fDleffVVtGvXDgAwadIkKBQKJCUloV27dqhRowbee+895OTkICoqCvXr18eff/6ptcDo+++/j6ioKIwfPx4dO3bEzp07sWfPHmzatAkhISEG9Ulq76NYPSlU4etD1/D1oWsoLKWEi3q2z7EZvTgQZmZSO7fFkFGGUA+kV6tWDTk5ObCzs0O3bt0QFRWFDh06AACOHz+OwMBA/PDDD3jttde0Xt+tWzc8evQIp0+fNuj3Se19NIRKJeC7EzexMOYyHhcUwcXBDu8HNcObnRtCzlywGdZ4bqtJJc9MwZrfR7JOht41KcZzu9ylXRwdHTF8+HB88cUXVh9GRCQ9zCgiEjMxZNSiRYtw69YtzeMdO3Zgx44dAIDQ0FAoFAp4e3vjyJEjmDp1KmbOnAlHR0f0798fixcv1hpEB4AFCxagZs2aWLFiBdatW4cmTZpg48aNBg+ik+k42ssR3qcp6rg6Y9ZPF0tsJwBQZuYhLjENnRu7V14HSfTEkFGGcHR0xNChQ9GvXz/UqlULCQkJWLRoEbp164Y//vgD7dq106zVoF734WleXl44evRoifvPz8/XWkg5KyvL9AdhQTdTc/H+jxcQl1g8C71TIzd8NqwNGrhXLeOVRNIhlTwjskVBrbzQx89Tknc6lXsgXSaToUmTJkhNTTVHf4iIKoQZRURiJoaMunnzpkHtWrZsiX379pXZTi6XIyIiAhERERXsGZlKVaeyy+kAwP3svLIbkU0RQ0YZokuXLujSpYvm8aBBgzBs2DC0adMGERERiImJwePHjwFA5+IfADg7O2ue12f+/PmIjIw0fcctrEglYO3xRCz69TLyClSo4miHiJebY1SnBpyFTlZHKnlGZKvs5DJJTuiQG/OiWbNmYenSpbh8+bKp+0NEVGHMKCISM2YUmVvt6s4mbUe2RaoZ5evri8GDB+PQoUMoKiqCi4sLAGjNLFfLy8vTPK9PREQEMjMzNT9JSUlm63dluf4gB6+tOIFP9lxCXoEKXX3dsW9Kd7zOUi5kxaSaZ0QkXuWekQ4AJ0+ehLu7O1q1aoUXXngBDRs21PkgIpPJ8MUXX5ikk0RE5cGMIiIxY0aRuQU0coOXwhl3M/Ogr1K6ukZ6QCO3yu4aSYCUM8rb2xtPnjxBbm6upqSLusTL05RKJerWrVvifpycnPTOZJeiIpWA1UdvYMn+K8gvVKGakz1m9WuB4ABvyGQcQCfrJuU8IyJxKvdio0DxLbxl7lgmQ1FRkVGdkiIxFsAny7C2Fe2leG4zo3RJ8X0k82BGWR4zSpcU30exi4lXImzjGQDQGkxX/2tfHtpeazEnMg8pnttSzqhhw4Zhz549yM3NRXZ2NmrVqoXw8HB89tlnmjZPnjyBu7s7XnvtNXz77bcG7VeK7yMAXL2XjenbL+BcUgYAoHtTD8x/tTXq1Sh5Nj7ZFqme24aScp6Vh7W/j2S7xHhuGzUjXaVSmbofRFbB0JWHybyYUUT6MaPEgRlFlSGolReWh7bX+TfvyX/zVAYpZNSDBw/g4eGhte38+fP4+eef8fLLL0Mul0OhUKB3797YuHEjPvzwQ1SvXh0AsGHDBuTk5GD48OGW6LrZFakEnLj+EFv+vI2Y+LsoVAmo7mSPDwf4YXiH+pyFTjZFCnlGRNJi1ED67du34eHhUWJducePH+PBgwfw8fGpUOeIpEQ98+vZWzzuZuYhbOMZzvyqRMwoIl3MKPFgRlFlCWrlhT5+nlZ1FwqZnxQyasSIEXBxcUGXLl1Qu3ZtJCQkYOXKlahSpQoWLFigaffpp5+iS5cu6NGjB8aPH4/k5GQsXrwYffv2RVBQkMX6by4x8UrM+fkv3Mv6py68k70cs/o1x2sdvS3YMyLLEHueHT58GD179tT73IkTJ/D8889Xco+IqCxGLTbaqFEj/PTTTyU+//PPP6NRo0ZGd4pIaopUAiKjE/TWIVVvi4xOQJGq3JWUyAjMKCJtzChxYUZRZbKTy9C5sTsG+9dD58buHESnMkkho4YMGYLU1FQsWbIEEyZMwA8//IBXX30Vp06dQosWLTTt2rdvjwMHDsDFxQXh4eFYuXIl3nrrLWzfvt2CvTePmHgl3tl4RmsQHQDyC1WY9VM8YuJ1a8UTWTsp5BkATJ48GRs2bND68fX1tXS3iEgPo2akl1VWvaCgwKBaVETWIi4xTeu26WcJAJSZeYhLTEPnxu6V1zEbxYwi0saMEhdmFBGJmRQyavLkyZg8ebJBbQMDA3H8+HEz98iyilQC5v78V6ltIqMT0MfPkxfTyKZIIc8AoFu3bhg2bJilu0FEBjB4ID0rKwsZGRmaxw8fPsTt27d12mVkZGDLli2aVdKJbMH97JIHqIxpR+XHjCIqGTPK8phRRCRmzChpi73xEHefmYn+NF4wJ1si1TzLzs6Gi4sL7O2Nmu9KRJXE4H+hn3/+OT766CMAxasaT5kyBVOmTNHbVhAEfPLJJybpIJEU1K7ubNJ2VH7MKKKSMaMsjxlFRGLGjJK27+N0Bwn14QVzsgVSzLMxY8YgJycHdnZ26NatG6KiotChQ4cS2+fn5yM//5+LZ1lZWZXRTZKAIpXAtXHMzOCB9L59+6JatWoQBAHvv/8+goOD0b59e602MpkMVatWxb/+9a9S/9ETWZuARm7wUjjjbmae3hrEMgCeiuIQI/MQS0bl5OQgKioKsbGxiIuLQ3p6OtauXYvRo0frtL106RLCw8Nx7NgxODo6on///liyZAk8PDy02qlUKixatAjLly+HUqlE06ZNERERgeDgYLMcA1kfZpTliSWjiIj0YUZJ19ZTSdh9wbD657xgTrZASnnm6OiIoUOHol+/fqhVqxYSEhKwaNEidOvWDX/88QfatWun93Xz589HZGRkJfeWxC4mXonI6AStkp5eCmfMGeiHoFbiuPPCGhg8kN65c2d07twZAJCbm4uhQ4eiVatWZusYkZTYyWWYM9APYRvPQAZoDVSpr/3NGejHK4FmJJaMSk1NxUcffQQfHx+0bdsWhw8f1tsuOTkZ3bt3h0KhwLx585CTk4NFixbh4sWLiIuLg6Ojo6bt7NmzsWDBAowbNw4dO3bErl27EBISAplMhpEjR1bSkZGUMaMsTywZRUSkDzNKmg4k3EPEjosAgKpOdniUX8QL5mTzpJRnXbp0QZcuXTSPBw0ahGHDhqFNmzaIiIhATEyM3tdFRERg6tSpmsdZWVnw9vY2e39JvGLilQjbeEbnb8DdzDyEbTyD5aHtOZhuIjKhrNUXyCBZWVlQKBTIzMyEq6urpbtDFmKNVwB5bpdPfn4+0tPT4enpiVOnTqFjx456Z6RPmDAB69atw99//w0fHx8AwIEDB9CnTx+sWLEC48ePBwCkpKSgUaNGGD9+PJYuXQqg+BbEHj16IDExETdv3oSdnV2Z/eL7SAAzisSL7yNZK57b1kGM7+PpW2kIWRWL/EIVhravj94tPDBh01kA+i+YcxCF9BHjuU1AcHAwduzYgUePHvG7HpWpSCUgcOFBre94T1NfTD02o5fkJk6J8dw2ehWD9PR0bN68GTdu3EB6errOasgymQzffvtthTtIJCVBrbzQx8+TNalEwFIZ5eTkBE9PzzLb/fjjjxgwYIBmEB0AevfujaZNm2Lr1q2agfRdu3ahoKAAEyZM0Op7WFgYQkJCcOLECQQGBpr8OMg6MaPEg5+jiPRjbU9xYEaJ25V72Ri77hTyC1Xo2cwDC4a2hoOdHMtDZToXzD0lfsGcqKKkmGfe3t548uQJcnNzRTN4SOIVl5hW4iA6wAWnTc2ogfR9+/Zh2LBhmn/UNWvW1Gkjk5nnAy/rD5PY2cllDCcLs2RGGSIlJQX379/XW48vICAAe/fu1Tw+e/YsqlatihYtWui0Uz+vbyCdC9BQSZhRlif2jCKyFGu8a0aKmFHidifjMd5cE4fMxwVo51MDX49qDwc7OQBeMCd6llTz7MaNG3B2dka1atUs3RWSAEMXkuaC06Zh1ED6f/7zH3h6emLHjh1o3bq1qftUKtYfJqKyWDKjDKFUFi8I5eWlOyjg5eWFtLQ05Ofnw8nJCUqlEnXq1NH5gKd+7Z07d/T+Di5AQyReYs8oIktgbU/xYEaJj/pOjcTUHHx96DqUmXlo7FEVa97siCqO2l/pecGc6B9iz7MHDx7oTPQ8f/48fv75Z7z88suQy+UW6hlJiaELSXPBadMwaiD92rVriIqKskgQeXl5QalUatUf1mfevHnIzc3F6dOnNaUTAgIC0KdPH6xbt06r/vDixYsxceJETf3ht99+Gz169MD06dMxfPhwg2pSEZF4WDKjDPH48WMAxWVgnuXs7Kxp4+TkpPnf0trpwwVoiMRL7BlFVNmKVAIioxP0LpIooLi2Z2R0Avr4eXJmbSVgRomLvjs15DLgrcBGqFnVsZRXEpHY82zEiBFwcXFBly5dULt2bSQkJGDlypWoUqUKFixYYOnukUQENHKDl8IZdzPzuOB0JTDq8laTJk2QnZ1t6r4YxFT1h9VKqz+cnJyMEydOmPYAiMjsLJlRhnBxcQEArdIranl5eVptXFxcDGr3LCcnJ7i6umr9EElNkUrAiesPsetcCk5cf4gilXWsjy72jCKqbOWp7Unmx4wSD/WdGs/++1AJwOyf4hETr7RQz4ikQex5NmTIEKSmpmLJkiWYMGECfvjhB7z66qs4deqUTmlPopLYyWWYM9APwD8LTKupH88Z6MfJCCZi1ED6J598gmXLluHmzZsm7o5plFV/+OzZs5rHhtQf1ic/Px9ZWVlaP0QkDmLPKHVZFnWJl6cplUq4ublpZqF7eXnh7t27OoviqF9bt25dM/eWyDJi4pUIXHgQwatO4r0t5xC86iQCFx60ikEDsWcUUWVjbU9xYUaJQ2l3aqhFRidYzUVmInMQe55NnjwZsbGxePjwIQoKCnDnzh1s2LABvr6+lu4aSUxQKy8sD20PT4V2+RZPhTPL45mYUaVdfvvtN3h4eKBFixbo06cPvL29dcqfyGQyfPHFFybpZHmx/jCRYdT1Fq1tMSKxZ1S9evXg4eGBU6dO6TwXFxcHf39/zWN/f3+sXr0aly5dgp+fn2Z7bGys5nkia2PttZLFnlFElY21PcWFGSUO5blTgzXRifRjnpEt4YLTlcOogXR1LXEA2L17t942lgwj1h8mKpu+eoteCmfMGegn6QEqQPwZBQBDhw7F+vXrkZSUpMmO3377DVeuXEF4eLim3eDBgxEeHo5ly5ZpjksQBHzzzTeoV68eunTpYpH+E5mLLdRKlkJGEVUm1vYUF2aUONxOyzWoHe/UICoZ84xsDRecNj+jBtJVKpWp+2FSlVV/WN8APJEUWPtsT0tn1NKlS5GRkaG5oyU6OhrJyckAgEmTJkGhUGDWrFnYtm0bevbsiffeew85OTmahXDGjBmj2Vf9+vUxZcoUREVFoaCgAB07dsTOnTtx9OhRbNq0iYshk9WxhRl4ls4oIrFR1/YM23gGMkDr8wlre1Y+ZpTlZT4qwMrfbxjUlndqEJWMeUZEpmbUQLrYlbf+8KFDhyAIglZ5F9YfJmtlC7M9LW3RokW4deuW5vGOHTuwY8cOAEBoaCgUCgW8vb1x5MgRTJ06FTNnzoSjoyP69++PxYsX61ykW7BgAWrWrIkVK1Zg3bp1aNKkCTZu3IiQkJBKPS6iysBayUS2SV3b89m75Tyt5G45IkOl5T7B69/G4vqDXMhkgFBCCXTeqUFERFT5KjSQfvLkSRw6dAj379/HhAkT0KRJEzx69Ah///03mjZtimrVqpmqn+XC+sNEJbOF2Z5qlsooQxezadmyJfbt21dmO7lcjoiICERERFSwZ0TiZ0u1ksX6OYrIUljbU1yYUZXvQXY+QlfH4vK9bNSq5oiwFxrjk92XAPBODaKKYJ4RkanIjXnRkydP8Oqrr6Jr166YPXs2vvzySyQlJRXvUC5H3759LV5jaujQodi9e7emX8A/9YeHDx+u2TZ48GA4ODhg2bJlmm2sP0zWzBZme0oho4hIP3Wt5JKGBWQoXs9ByjPwmFFEJVPX9hzsXw+dG7tzkNACmFGWcTczDyNWnsDle9mo4+qELeM7463A57A8tD08FdoXjz0VzpIvxUhUGZhnRGRqRg2kf/jhh9i9ezeWL1+Oy5cvQ3jqfjNnZ2cMHz4cu3btMlknn7V06VJ88sknWLNmDYDi+sOffPIJPvnkE2RmZgIAZs2ahSpVqqBnz5746quvMH/+fAwfPrzE+sNff/01/v3vf2P16tUYOHAgjh49is8++4z1h8nq2MJsT0tnFBEZT10rGYDOYLq1zMBjRhGRmDGjKl9KxmOMWHkCNx7koq7CGT+M7wzf2sUzZINaeeHYjF7YPO55fDHSH5vHPY9jM3pxEJ3IAMwzMoW/7mTifFKGpbtBImFUaZfNmzcjLCwM48ePx8OHD3Web9GiBbZt21bhzpWE9YeJjKee7Xk3M09vnXRrqLdo6Ywiooqx9lrJzCgiEjNmVOW6/fARgledRErGY3i7ueD7t5+Ht1sVrTbqOzWIqHyYZ1QRKRmPsfjXy/jpbApaeLpi96RAyCU8mYdMw6iB9Pv376N169YlPm9nZ4dHjx4Z3amysP4wkfHUsz3DNp6BDNZZb9HSGUVEFWfNtZKZUUTWoUglMKPIaEUqAbvOpiBydwIyHxegoXsVbB7/PLwULpbuGpHVYJ6RMTIfF2DZ4WtYe/wmnhSqAAC+tash90khqjs7WLh3ZGlGDaR7e3vj77//LvH548ePw9fX1+hOEZF5WftsT2YUkXWw1hl4zCgi6YuJV+p8jvLi5ygyUEy8EnN+/gv3svI12x49KcL5pAwOpBOZEPOMyuNJoQobTt7CVwevIuNRAQCgUyM3DGxbF9Wd7RGfkmU1F83JeEbVSA8JCcGKFStw4sQJzTaZrPhEWrVqFbZu3Yo33njDND0kIrOw5nqLzCgiEjNmFJG0xcQrEbbxjNYgOlC8WGTYxjOIiVdaqGemwYwyL/X58/QgOgA8yM63ivOHSEyYZ2QIQRAQff4Oei85go93JyDjUQF8a1fDhBca49bDXHywMx7vbTmH4FUnEbjwIHPaxsmEp1dbMNCTJ08wcOBAHDx4EC1atMBff/2F1q1bIy0tDcnJyejXrx927dplUwt1ZmVlQaFQIDMzE66urpbuDpHJSPHcZkbpkuL7SGQIKZ7bzChdUnwfyTYVqQQELjyoM4iupl5r5tiMXrCTyyR5bjOjdJnqfSzv+UNkblLMqPKwlTyz9vfRnGJvPMS8vZdwPjkTAOBR3QlT+zSFq7M93v3+rM66cupkXh7a3iomIYqdGM9to2akOzo6IiYmBmvXrsVzzz2H5s2bIz8/H23atMG6desQHR0t+SAiIuliRhGRmDGjiKQrLjGtxEFQoHjtGWVmHuIS0yqvUybGjDIfWzh/iMSEeUYluXY/G2+v/xMjVp7E+eRMVHG0Q3jvpjgy/QW81sEbn+y5pDOIDvyzxlxkdAKKVOWel0xWwKga6UDx7TChoaEIDQ01ZX+IiEyCGUVEYsaMIpKm+9klD4Ia006smFHmcS/rsUHtpH7+EIkJ84yedj87D5/vv4of/rwNlVC8JlNwgDfee7EpPKo7AQBOXH9o8EVPa1zPiUpn1Iz0tLQ0XLhwocTnL168iPT0dKM7RURUEcwoIhIzZhSRdNWu7mzSdmLEjDK9IpWAE9cfYvcFw+rqSvn8IRIT5hmp5eYX4vP9V/BC1GFsjiseRO/rVwf7pnTHJ0NaawbRAdu5aE7GMWpGenh4OC5fvoyTJ0/qff7f//43WrRogW+//bZCnSMiMgYziojEjBlFJF0BjdzgpXDG3cw8vbd8q2tcBzRyq+yumQwzyrRi4pWIjE4odXajmjWcP0RiwjyjwiIVfjiVhM/3X0VqTvEiz/7eNTCrX4sSs9YWLpqT8YyakX7w4EEMGjSoxOcHDhyIAwcOGN0pIqKKYEYRkZgxo4iky04uw5yBfgD+WXBMTf14zkA/SS8UyYwynZh4JcI2njF4EB2Q/vlDJCbMM9slCAL2J9zDS//7HbN/ikdqTj4auFfB1yHt8dOELqVesFRfNC8piWUAvHjR02YZNZD+4MED1KpVq8Tn3d3dcf/+faM7RURUEcwoIhIzZhSRtAW18sLy0PbwVGjPRPNUOGN5aHsEtfKyUM9MgxllGkUqAZHRCXrvXNDHWs4fIjFhntmmc0kZGLHyJMZ9dwrXH+SiZhUHzB3oh/3hPdC/jRdkstIvVtrCRXMynlGlXby8vHD27NkSnz99+jQ8PDyM7hQRUUUwo4hIzJhRRNIX1MoLffw8EZeYhvvZeahdvXhmmjV8qWZGmUZcYppBM9Hf7emLrr61rOb8IRIT5pntKFIJiD5/B+v/uImzSRkAACd7Od4KbIR3XmgMV2eHcu1PfdH82dJcngpnzBnox4ueNsyogfQhQ4bg66+/xssvv6xzm8yuXbuwdu1ahIWFmaSDRETlxYwiIjFjRhFZBzu5DJ0bu1u6GybHjDINQxeha1KnmlWeR0RiwDyzfgVFKiza9zfW/XEL+YUqzXYXBzv8d0ALBHdqYPS+rfmiORlPJgiCoXebaWRmZiIwMBAJCQlo27YtWrVqBQCIj4/H+fPn0aJFCxw7dgw1atQwdX9FKysrCwqFApmZmXB1dbV0d4hMRornNjNKlxTfRyJDSPHcZkbpkuL7SGQIKZ7bzChdxryPRy4/wJtr48pst3nc8xxIJ4uRYkaVh63kmbW/j/okpuZiy5+38X3sbWTnFeo8rx7qZsksaRPjuW1UjXSFQoGTJ0/igw8+QEFBAbZv347t27ejoKAAH374IWJjYyUfREQkXcwoIhIzZhQRiRkzynhFKgEnrj/EzrPJ+PbYjVLbcrE6IvNjnlmXvIIi7DqXgpErT6DnosNYceSG3kF0AJr1KSKjE1CkKvf8YaISGTUjnXSJ8SoJkSnw3LYOfB/JWvHctg58H8la8dy2Doa8jzHxSp1aumoyQGvRUc6UJLFgRlkHa38fL9/Nxua42/jpbAoyHxcAAOQyoE39Gjj3//XQS8M7f6RLjOe2UTXSiYiIiIiIiKh4ED1s4xmUNENNUcUBGY8KNI+5WB0RUely8wux+8IdbI5L0hosr6twxmsdvfFaB2/8eTMN7205V+a+DF2zgsgQRg+kX7p0CWvXrsWNGzeQnp6OZye2y2Qy/PbbbxXuIBGRMZhRRCRmzCgiEjNmlOGKVAIioxNKHESXAXC2l2PT252QmpPPxeqIKhnzTDoEQcCF5Exs+fM2fj53B7lPigAA9nIZereog5EB3ujWxEOTn7WrOxu0X0PbERnCqIH0DRs2YMyYMXBwcECzZs1Qs2ZNnTasGENElsKMIiIxY0YRkZgxo8rn5I2Hesu5qAkA7mblQy6TYbB/vcrrGBExzyQi83EBdp5NwZY/k3BJmaXZ3tC9CkZ09MHQf9XTOxge0MgNXgpn3M3M03sxU4biO4C4FgWZklED6XPnzkW7du3wyy+/oFatWqbuExFRhTCjiEjMmFFEJGbMKMPFxCsx88eLBrVlaQGiysc8Ey9BEPDnzXRsibuNPReVyC9UAQAc7eV4uZUnRnb0wfPPuUEmK/nuHTu5DHMG+iFs45kS16KYM9CPdwCRSRk1kH7nzh1MmzaNQUREosSMIiIxY0YRkZgxowxTVl30Z7G0AFHlY56JT9bjAkTtu4x9f93F/ex8zfZmdapjZIA3XmlXDzWqOBq8v6BWXlge2l5nsWeuRUHmYtRAeps2bXDnzh1T94WIyCSYUUQkZswoIhIzZlTZyqqL/jSWFiCyHOaZeKTnPsEHOy9ib/xdPF1Nx8XBDpNf9MU7PRqXOvu8NEGtvNDHzxNxiWm4n53HtSjIrOTGvGjJkiX49ttv8ccff5i6P0REFcaMIiIxk0JGHT58GDKZTO/PyZMntdr+8ccfCAwMRJUqVeDp6YnJkycjJyfHQj0nooqSQkZZWlxiWql10Z/F0gJElsE8s7y7mXn4eHcCnp//G/Zc1B5EB4C8giJ8FlM8Q70i7OQydG7sjsH+9dC5sTszl8zGqBnpCxcuhEKhQLdu3eDn5wcfHx/Y2dlptZHJZNi1a5dJOklEVB7MKCISMyll1OTJk9GxY0etbb6+vpr/f+7cObz44oto0aIFlixZguTkZCxatAhXr17FL7/8UtndJSITkFJGWUKRSsDxa6kGta1RxQELXm3N0gJEFsI8qxxFKkFnNvith7lYceQGdpxNRkFRyffvCCi+cycyOgF9/Dw5AE6iZ9RA+oULFyCTyeDj44OcnBwkJCTotDH2lgwioopiRhGRmEkpo7p164Zhw4aV+PysWbNQs2ZNHD58GK6urgCAhg0bYty4cfj111/Rt2/fyuoqEZmIlDKqsu1PuItFh/40eDb618Ht0bUJazMTWQrzzPxi4pU69cmdHeTIL1Bpyl8196yOv+9ml7gPAYAyMw9xiWno3NjdvB0mqiCjBtJv3rxp4m4QEZkOM4qIxExqGZWdnQ0XFxfY22t/bMzKysL+/fsRHh6uGUQHgDfeeAPh4eHYunUrB9KJJEhqGVWZpv5wHjKnKmW2U9dFf54DQkQWxTwzr5IWXc4rUAEAWtdzxdxBLZGc/hjvbTlX5v7uZxteMovIUoyqkU5ERERE1m/MmDFwdXWFs7MzevbsiVOnTmmeu3jxIgoLC9GhQwet1zg6OsLf3x9nz54tcb/5+fnIysrS+iEiEjtDFxcFWBediKStSCXgxPWH2HUuBSeuP0SRSjsB03OfYOaPF0vNxdScJ/D3rona1Z0N+p2GtiOyJKNmpKsdOXIEe/bswa1btwAADRo0QP/+/dGjRw+TdI6IqCKYUUQkZmLOKEdHRwwdOhT9+vVDrVq1kJCQgEWLFqFbt274448/0K5dOyiVSgCAl5du7V8vLy8cPXq0xP3Pnz8fkZGRZus/EVWcmDNKzDwVzpgz0I910YlEhHlWumdrnKfnPsHHe7TLtXgpnDGply8KigTsT7hXPLj+7Mqhz1CXawlo5AYvhTPuZubpHXhX38UT0MjNtAdGZAYyQSjjzNfjyZMnCA4Oxs6dOyEIAmrUqAEAyMjIgEwmwyuvvILNmzfDwcHB1P0VraysLCgUCmRmZmrd3kwkdVI8t5lRuqT4PhIZQorntlQz6tq1a2jTpg26d++OmJgYbNiwAW+88QZiY2MREBCg1faNN97Azz//jIyMDL37ys/PR35+vuZxVlYWvL29JfU+EhmCGWUd1O+j95StkJdS2uXdno0R3qcZZ6KTZEgxo8rDVvLM0PdR36Kg+xPu6tQ4N6UvRvpjsH89TRkYQPvuHnVaLg9tzwuQpEOMGWVUaZfIyEj89NNP+M9//gOlUom0tDSkpaXh7t27mDZtGnbs2IGPPvrI1H0lIjIIM4qIxEyqGeXr64vBgwfj0KFDKCoqgouLCwBoDYir5eXlaZ7Xx8nJCa6urlo/RCQOUs2okuTn52PGjBmoW7cuXFxc0KlTJ+zfv98sv6urrwcH0YlERAp5ZsqMiruRhieFKq2SLOrHH0X/hY6fHkDwqpN4b8s5BK86iX99sh/vbDxTrkF0BzsZZr7cHEtea2tQe3W5lqBWXlge2h6eCu3yLZ4KZw6ik6QYNSO9UaNGeOGFF7B27Vq9z48ePRqHDx+2qYUdxHiVhMgUpHhuM6N0SfF9JDKEFM9tKWfU+++/j6ioKGRmZuLixYsIDAzEDz/8gNdee02rXbdu3fDo0SOcPn3aoP1K8X0kMoQUz20pZ5Q+wcHB2L59O6ZMmYImTZpg3bp1+PPPP3Ho0CEEBgYatI+yZqSryxIcm9GLA+kkKVLMqPKQQp6ZOqPsnavg6XLmchmgKveoX+k2j3seAY3cELjwYJnlWp7NRX2z4pmbVBIxZpRRM9KVSiU6depU4vOdOnXC3bt3je4UEVFFMKOISMyknFE3btyAs7MzqlWrhlatWsHe3l5rAVKg+Dbqc+fOwd/f3zKdJKIKkXJGPSsuLg5btmzB/PnzERUVhfHjx+PgwYNo0KAB3n//fZP8Di4uSiReYs8zc2TUs4Pmph5EB4D72Xmwk8swZ6AfgH9yUK20XLSTy9C5sTsG+9dD58buzE2SHKMG0uvXr4/Dhw+X+PyRI0dQv359Y/tERFQhzCgiEjMpZNSDBw90tp0/fx4///wz+vbtC7lcDoVCgd69e2Pjxo3Izs7WtNuwYQNycnIwfPjwyuwyEZmIFDLKUNu3b4ednR3Gjx+v2ebs7Iy33noLJ06cQFJSUoV/B8sSEImX2POsMjLKHFiuhWyZUQPpb775JrZu3Yp33nkHly9fRlFREVQqFS5fvoywsDBs27YNo0ePNnFXiYgMI4WMOnz4MGQymd6fkydParX9448/EBgYiCpVqsDT0xOTJ09GTk6OhXpORBUlhYwaMWIE+vfvj08//RSrVq1CeHg4unTpgipVqmDBggWadp9++inS0tLQo0cPfPPNN/jggw/w7rvvom/fvggKCrLgERCRsaSQUYY6e/YsmjZtqnM7uHqB5HPnzul9XX5+PrKysrR+9Pmwfwscm9GLg0VEIiX2PDN3RpmaDICXorgci1pQKy8cm9ELm8c9jy9G+mPzuOeZi2TV7I150axZs3D9+nWsXLkSq1atglxePB6vUqkgCALefPNNzJo1y6QdJSIylJQyavLkyejYsaPWNl9fX83/P3fuHF588UW0aNECS5YsQXJyMhYtWoSrV6/il19+qezuEpEJSCGjhgwZgk2bNmHJkiXIysqCh4cHXn31VcyZM0cro9q3b48DBw5gxowZCA8PR/Xq1fHWW29h/vz5Fuw9EVWEFDLKUEqlEl5euoM56m137tzR+7r58+cjMjKyzP3Xqu7EsgREIib2PDN3RpmSIeVaiGyBUQPpdnZ2WLduHaZOnYq9e/fi1q1bAIAGDRqgX79+aNOmjUk7SURUHlLKqG7dumHYsGElPj9r1izUrFkThw8f1sxUaNiwIcaNG4dff/0Vffv2rayuEpGJSCGjJk+ejMmTJxvUNjAwEMePHzdzj4ioskghowz1+PFjODk56Wx3dnbWPK9PREQEpk6dqnmclZUFb29vnXbq8gZEJE5izzNzZ5QpeSqcMWegH2eak80zaiBdrU2bNhYPHiKikkglo7Kzs+Hi4gJ7e+1IzsrKwv79+xEeHq51u98bb7yB8PBwbN26lQPpRBImlYwiIttkDRnl4uKC/Px8ne15eXma5/VxcnLSO7ilJkPxoNLT5Q2ISLzEmmfmyihDyQAIAGpUcUDGowLNdi+FMz7s3wI1qzrhfnYealcvzjvegUNUjhrpeXl5eOedd/DVV1+V2u7LL79EWFgYCgoKSm1HRGRKUs2oMWPGwNXVFc7OzujZsydOnTqlee7ixYsoLCxEhw4dtF7j6OgIf39/nD17tsT9WqpuHhHpJ9WMIiLbYK0Z5eXlBaVSqbNdva1u3brl3mdp5Q2IyPKklGfmyKjy8FQ445vQ9jj9QR+dGuf92tRF58buGOxfD50buzPviP6fwQPpK1euxLp169C/f/9S2/Xv3x9r167F6tWrK9w5IiJDSS2jHB0dMXToUHzxxRfYtWsXPvnkE1y8eBHdunXTDJCrP0CVVDevpJp5QHHdPIVCofkx961+RFQ6qWUUEdkWa80of39/XLlyRWdCQWxsrOb58vJUOGN5aHuWNyASKSnlmTky6tnx7mcfu1V1wFtdG2otCqqucc5Bc6KyyQRBEAxpGBgYiAYNGmDTpk1ltn399ddx69Yt/P777xXuoFRkZWVBoVAgMzNTZ8VlIimTyrltDRl17do1tGnTBt27d0dMTAw2bNiAN954A7GxsZqV29XeeOMN/Pzzz8jIyNC7r/z8fK3bBNV188T+PhKVFzPKOkjlfSQqL6mc29aaUbGxsXj++ecRFRWFadOmASj+jNSqVSu4u7vj5MmTBu1H/T7uP5uInm0acJCJrIZUMqo8pJRn5sio7q18cPpWuqYky78a1NR6zBItJCVizCiDa6RfvHgRo0aNMqhtly5dEB0dbXSnpEh9PYLlE8jaqM9pA6+5WYw1ZJSvry8GDx6MHTt2oKioSFMTr6S6eSXVzAN06+Yxo8haMaOsAzOKrBUzyrI6deqE4cOHIyIiAvfv34evry/Wr1+Pmzdv4ttvvzV4P+r3r3kte+TmZJuru0SVTioZVR5SyjNzZFTeoxy09HBASw8HANB5zAwjKRFjRhk8kP7kyRM4Ojoa1NbR0VHvwI81e/jwIQCwfAJZrYcPH0KhUFi6GyWylozy9vbGkydPkJubqynpUlLdvPLUzGNGkbVjRkkbM4qsHTPKcr777jt8+OGH2LBhA9LT09GmTRvs3r0b3bt3N3gfzCiydmLPqPKQWp4xo4jKJqaMMnggvW7duoiPjzeobXx8vNkXRRAbN7fiFdtv374tmjfX3NSlIpKSkkRzi4W52eIxZ2ZmwsfHR3OOi5W1ZNSNGzfg7OyMatWqoVWrVrC3t8epU6fw2muvado8efIE586d09pWFmaUbfx7tcVjZkZZB2aUbfx7tcVjZkZZnrOzM6KiohAVFWX0PphRtvHv1RaPWSoZVR5SyzNmlHFs8d+rLR6zGDPK4IH03r1747vvvkNERARq165dYrv79+/ju+++w/Dhw03SQamQy4vXbVUoFDZzQqu5urrymG2A+hwXK6ll1IMHD+Dh4aG17fz58/j555/x8ssvQy6XQ6FQoHfv3ti4cSM+/PBDVK9eHQCwYcMG5OTklOsYmFE8ZmvHjJI2ZhSP2doxo6SNGcVjtnZiz6jysMU8Y0bxmK2dmDLK4J7MmDEDeXl56NWrl2YF4WfFxsbixRdfRF5eHqZPn26yThIRlUVqGTVixAj0798fn376KVatWoXw8HB06dIFVapUwYIFCzTtPv30U6SlpaFHjx745ptv8MEHH+Ddd99F3759ERQUZMEjIKLykFpGEZFtYUYRkbVgnhGRORk8I/25557D1q1bERwcjC5duuC5555D69atUb16dWRnZyM+Ph7Xr19HlSpVsGXLFjRu3Nic/SYi0iK1jBoyZAg2bdqEJUuWICsrCx4eHnj11VcxZ84c+Pr6atq1b98eBw4cwIwZMxAeHo7q1avjrbfewvz58y3YeyIqL6llFBHZFmYUEVkL5hkRmZVQTomJicI777wj1K9fX5DJZJqfevXqCf/+97+F69evl3eXViEvL0+YM2eOkJeXZ+muVBoes22Q2jEzo/ST2vtoCjxm2yC1Y2ZG6Se199EUeMy2QWrHzIzST2rvoynwmG2DNR+zLeWZNb+PJeEx2wYxHrNMEATB2EH47OxsZGVlwdXVVVO7l4hILJhRRCRmzCgiEjNmFBFZC+YZEZlKhQbSiYiIiIiIiIiIiIisnXiWPSUiIiIiIiIiIiIiEiEOpBMRERERERERERERlYID6UREREREREREREREpeBAOhERERERERERERFRKTiQXkH5+fmYMWMG6tatCxcXF3Tq1An79++3dLc0cnJyMGfOHAQFBcHNzQ0ymQzr1q3T2/bSpUsICgpCtWrV4Obmhtdffx0PHjzQaadSqfDZZ5+hUaNGcHZ2Rps2bbB58+ZK22dp/vzzT7z77rto2bIlqlatCh8fH7z22mu4cuWKVR4vAPz1118YPnw4nnvuOVSpUgW1atVC9+7dER0dbbXHTIZjRonr/GVGMaNIGzNKXOcvM4oZRdqYUeI6f5lRzCjSxowS1/nLjLKRjBKoQkaOHCnY29sL06ZNE1asWCF07txZsLe3F44ePWrprgmCIAiJiYkCAMHHx0d44YUXBADC2rVrddolJSUJtWrVEho3bix88cUXwqeffirUrFlTaNu2rZCfn6/VdubMmQIAYdy4ccLKlSuF/v37CwCEzZs3m32fZRk6dKjg6ekpTJo0SVi1apXw8ccfC3Xq1BGqVq0qXLx40eqOVxAEYc+ePcJLL70kzJ07V1i5cqXwv//9T+jWrZsAQFixYoVVHjMZjhklrvOXGcWMIm3MKHGdv8woZhRpY0aJ6/xlRjGjSBszSlznLzPKNjKKA+kVEBsbKwAQoqKiNNseP34sNG7cWOjcubMFe/aPvLw8QalUCoIgCH/++WeJwRUWFia4uLgIt27d0mzbv3+/zsmfnJwsODg4CBMnTtRsU6lUQrdu3YT69esLhYWFZt1nWY4fP67zD+bKlSuCk5OTMGrUKKs73pIUFhYKbdu2FZo1a2Yzx0y6mFHFxHT+MqOKMaNIEJhRamI6f5lRxZhRJAjMKDUxnb/MqGLMKBIEZpSamM5fZlQxa88oDqRXwPTp0wU7OzshMzNTa/u8efMEAMLt27ct1DP9Sguu2rVrC8OHD9fZ3rRpU+HFF1/UPP76668FAMJff/2l1e77778XAGhd+TTHPo3Vvn17oX379mbtm5iOVxAEYcCAAUKdOnXM2j+xHTNpY0b9Q+znLzPKNo6ZtDGj/iH285cZZRvHTNqYUf8Q+/nLjLKNYyZtzKh/iP38ZUZZ1zGzRnoFnD17Fk2bNoWrq6vW9oCAAADAuXPnLNCr8ktJScH9+/fRoUMHnecCAgJw9uxZzeOzZ8+iatWqaNGihU479fPm2qexBEHAvXv3UKtWLbP1TQzHm5ubi9TUVFy/fh2ff/45fvnlF7z44otm658YjplKx4zSbqd+3lz7NBYzynqPmUrHjNJup37eXPs0FjPKeo+ZSseM0m6nft5c+zQWM8p6j5lKx4zSbqd+3lz7NBYzyvqOmQPpFaBUKuHl5aWzXb3tzp07ld0loyiVSgAo8VjS0tKQn5+vaVunTh3IZDKddsA/x2yOfRpr06ZNSElJwYgRI8zWNzEc73/+8x94eHjA19cX06ZNwyuvvIKlS5da9TFT6ZhR2u0AcZ6/zCjrPWYqHTNKux0gzvOXGWW9x0ylY0ZptwPEef4yo6z3mKl0zCjtdoA4z19mlPUdMwfSK+Dx48dwcnLS2e7s7Kx5XgrU/TTkWAw9ZnPs0xh///03Jk6ciM6dO+PNN980W9/EcLxTpkzB/v37sX79erz88ssoKirCkydPzNY/MRwzlc5a/rtb8/nLjGJG2TJr+e9uzecvM4oZZcus5b+7NZ+/zChmlC2zlv/u1nz+MqOsM6M4kF4BLi4umiscT8vLy9M8LwXqfhpyLIYeszn2WV53795F//79oVAosH37dtjZ2Zmtb2I43ubNm6N379544403sHv3buTk5GDgwIEQBMFqj5lKZy3/3a31/GVGMaNsnbX8d7fW85cZxYyyddby391az19mFDPK1lnLf3drPX+ZUdabURxIrwAvLy/N7QRPU2+rW7duZXfJKOrbGUo6Fjc3N83VGy8vL9y9exeCIOi0A/45ZnPsszwyMzPx8ssvIyMjAzExMVr7sMbj1WfYsGH4888/ceXKFZs5ZtLGjNJuB4jn/GVGMaOIGfVsO0A85y8zihlFzKhn2wHiOX+ZUcwoYkY92w4Qz/nLjLLujOJAegX4+/vjypUryMrK0toeGxureV4K6tWrBw8PD5w6dUrnubi4OK3j8Pf3x6NHj3Dp0iWtds8eszn2aai8vDwMHDgQV65cwe7du+Hn56f1vLUdb0nUt6dkZmbazDGTNmbUP8R0/jKjijGjiBn1DzGdv8yoYswoYkb9Q0znLzOqGDOKmFH/ENP5y4wqZtUZJZDRTp48KQAQoqKiNNvy8vIEX19foVOnThbsmX5//vmnAEBYu3atznPvvPOO4OLiIty+fVuz7cCBAwIAYfny5ZptSUlJgoODgzBx4kTNNpVKJXTr1k2oV6+eUFhYaNZ9lqWwsFAYNGiQYG9vL+zZs6fEdtZyvIIgCPfu3dPZ9uTJE6F9+/aCi4uLkJ2dbXXHTIZhRhUT0/nLjCrGjCJBYEapien8ZUYVY0aRIDCj1MR0/jKjijGjSBCYUWpiOn+ZUcWsPaM4kF5Bw4cPF+zt7YXp06cLK1asELp06SLY29sLR44csXTXNL766ivh448/FsLCwgQAwquvvip8/PHHwscffyxkZGQIgiAIt2/fFtzd3YXGjRsLX375pTBv3jyhZs2aQuvWrYW8vDyt/U2fPl0AIIwfP15YtWqV0L9/fwGAsGnTJq125thnWd577z0BgDBw4EBhw4YNOj/m7JsljlcQBGHIkCFCr169hLlz5wqrVq0SPv74Y6F58+YCAGHx4sVWecxkOGaUuM5fZhQzirQxo8R1/jKjmFGkjRklrvOXGcWMIm3MKHGdv8wo28goDqRX0OPHj4Vp06YJnp6egpOTk9CxY0chJibG0t3S0qBBAwGA3p/ExERNu/j4eKFv375ClSpVhBo1agijRo0S7t69q7O/oqIiYd68eUKDBg0ER0dHoWXLlsLGjRv1/m5z7LM0PXr0KPFYn70BwxqOVxAEYfPmzULv3r2FOnXqCPb29kLNmjWF3r17C7t27aqU/lnimMlwzChxnb/MKGYUaWNGiev8ZUYxo0gbM0pc5y8zihlF2phR4jp/mVG2kVEyQXim2joREREREREREREREWlwsVEiIiIiIiIiIiIiolJwIJ2IiIiIiIiIiIiIqBQcSCciIiIiIiIiIiIiKgUH0omIiIiIiIiIiIiISsGBdCIiIiIiIiIiIiKiUnAgnYiIiIiIiIiIiIioFBxIJyIiIiIiIiIiIiIqBQfSiYiIiIiIiIiIiIhKwYF0IiIiIiIiIiIiIqJScCCdiIiIiIiIiIiIiKgUHEgnIiIiIiIiIiIiIioFB9KJiIiIiIiIiIiIiErBgXQiIiIiIiIiIiIiolJwIJ2IiIiIiIiIiIiIqBQcSCciIiIiIiIiIiIiKgUH0omIiIiIiIiIiIiISsGBdCIiIiIiIiIiIiKiUnAgncxq9OjRaNiwoaW7oTF37lzIZDLIZDJUq1atXK/NyMjQvFYmk2HRokVm6iURVRZmFBGJGTOKiMSMGUVEYsaMInOwt3QHSHpkMplB7Q4dOmTmnhhvw4YNcHBwKNdrqlatig0bNiA1NRXh4eFm6hkRVRQzihlFJGbMKGYUkZgxo5hRRGLGjGJGWRoH0qncNmzYoPX4u+++w/79+3W2t2jRAqtWrYJKparM7hkkNDS03K9xcHBAaGgobt68yeAiEjFmFDOKSMyYUcwoIjFjRjGjiMSMGcWMsjQOpFO5PfuP/uTJk9i/f79RYUBEZGrMKCISM2YUEYkZM4qIxIwZRZbGGulkVs/WpLp586amntPXX3+N5557DlWqVEHfvn2RlJQEQRDw8ccfo379+nBxccHgwYORlpams99ffvkF3bp1Q9WqVVG9enX0798ff/31V4X6eurUKbz00kuoVasWXFxc0KhRI4wdO7ZC+yQicWNGEZGYMaOISMyYUUQkZswoMgfOSCeL2LRpE548eYJJkyYhLS0Nn332GV577TX06tULhw8fxowZM3Dt2jV89dVXmDZtGtasWaN57YYNG/Dmm2/ipZdewsKFC/Ho0SMsX74cgYGBOHv2rFGLSdy/fx99+/aFh4cHZs6ciRo1auDmzZvYsWOHCY+aiKSCGUVEYsaMIiIxY0YRkZgxo6giOJBOFpGSkoKrV69CoVAAAIqKijB//nw8fvwYp06dgr198an54MEDbNq0CcuXL4eTkxNycnIwefJkvP3221i5cqVmf2+++SaaNWuGefPmaW031B9//IH09HT8+uuv6NChg2b7J598UsEjJSIpYkYRkZgxo4hIzJhRRCRmzCiqCJZ2IYsYPny4JrQAoFOnTgCK612pQ0u9/cmTJ0hJSQEA7N+/HxkZGQgODkZqaqrmx87ODp06dTJ6ZeYaNWoAAHbv3o2CggIjj4qIrAUziojEjBlFRGLGjCIiMWNGUUVwRjpZhI+Pj9ZjdYh5e3vr3Z6eng4AuHr1KgCgV69eevfr6upqVH969OiBoUOHIjIyEp9//jleeOEFDBkyBCEhIXBycjJqn0QkXcwoIhIzZhQRiRkziojEjBlFFcGBdLIIOzu7cm0XBAEAoFKpABTXpfL09NRp9/TVw/KQyWTYvn07Tp48iejoaOzbtw9jx47F4sWLcfLkSVSrVs2o/RKRNDGjiEjMmFFEJGbMKCISM2YUVQQH0klSGjduDACoXbs2evfubfL9P//883j++efx6aef4vvvv8eoUaOwZcsWvP322yb/XURkfZhRRCRmzCgiEjNmFBGJGTOKANZIJ4l56aWX4Orqinnz5umtHfXgwQOj9puenq65yqjm7+8PAMjPzzdqn0Rke5hRRCRmzCgiEjNmFBGJGTOKAM5IJ4lxdXXF8uXL8frrr6N9+/YYOXIkPDw8cPv2bezZswddu3bF0qVLy73f9evXY9myZXjllVfQuHFjZGdnY9WqVXB1dUW/fv3McCREZI2YUUQkZswoIhIzZhQRiRkzigAOpJMEhYSEoG7duliwYAGioqKQn5+PevXqoVu3bhgzZoxR++zRowfi4uKwZcsW3Lt3DwqFAgEBAdi0aRMaNWpk4iMgImvGjCIiMWNGEZGYMaOISMyYUSQTnr1/gMiKzZ07F5GRkXjw4AFkMhnc3d0Nfq0gCHj48CGSkpLQvn17REVFYdq0aWbsLRHZGmYUEYkZM4qIxIwZRURixoyyDpyRTjbJw8MDVatWRU5OjsGvyczMhIeHhxl7RURUjBlFRGLGjCIiMWNGEZGYMaOkjTPSyabcuHEDN27cAADY29vjhRdeMPi1hYWFOHz4sOZx06ZN4ePjY+IeEpEtY0YRkZgxo4hIzJhRRCRmzCjrwIF0IiIiIiIiIiIiIqJSyC3dASIiIiIiIiIiIiIiMeNAOhERERERERERERFRKTiQTkRERERERERERERUCg6kExERERERERERERGVggPpRERERERERERERESl4EA6EREREREREREREVEpOJBORERERERERERERFQKDqQTEREREREREREREZWCA+lERERERERERERERKX4P51RIvMJjU+6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x450 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x450 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x450 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x450 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(res_original_data, res_interpolated_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouping = [179, 183, 154, 178, 156, 191, 344, 177, 182, 342, 456, 341, 147, 155, 152, 153, 181, 150, 5, 151, 176, 452]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_mechanism_df = data_df[data_df['Mechanism'] == str((29, 217))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
       "      <th>Ea3</th>\n",
       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "      <th>Fast_rxn1</th>\n",
       "      <th>Medium_rxn1</th>\n",
       "      <th>Slow_rxn1</th>\n",
       "      <th>Fast_rxn2</th>\n",
       "      <th>Medium_rxn2</th>\n",
       "      <th>Slow_rxn2</th>\n",
       "      <th>Reaction_order</th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214034</th>\n",
       "      <td>273</td>\n",
       "      <td>0.023977</td>\n",
       "      <td>125</td>\n",
       "      <td>0.062190</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214035</th>\n",
       "      <td>273</td>\n",
       "      <td>0.053240</td>\n",
       "      <td>56</td>\n",
       "      <td>9.835595</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214036</th>\n",
       "      <td>273</td>\n",
       "      <td>0.021213</td>\n",
       "      <td>14</td>\n",
       "      <td>134.300199</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214037</th>\n",
       "      <td>273</td>\n",
       "      <td>1.815907</td>\n",
       "      <td>161</td>\n",
       "      <td>0.015825</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214038</th>\n",
       "      <td>273</td>\n",
       "      <td>3.325518</td>\n",
       "      <td>25</td>\n",
       "      <td>1.991739</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768531</th>\n",
       "      <td>273</td>\n",
       "      <td>3.648937</td>\n",
       "      <td>58</td>\n",
       "      <td>1.190083</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768532</th>\n",
       "      <td>273</td>\n",
       "      <td>9.533488</td>\n",
       "      <td>156</td>\n",
       "      <td>845.269909</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768533</th>\n",
       "      <td>273</td>\n",
       "      <td>823.826795</td>\n",
       "      <td>56</td>\n",
       "      <td>0.091007</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768534</th>\n",
       "      <td>273</td>\n",
       "      <td>786.118977</td>\n",
       "      <td>89</td>\n",
       "      <td>3.026795</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768535</th>\n",
       "      <td>273</td>\n",
       "      <td>546.467301</td>\n",
       "      <td>157</td>\n",
       "      <td>449.655166</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}</td>\n",
       "      <td>(29, 217)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5183 rows × 257 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Temperature          A1  Ea1          A2  Ea2  A3  Ea3  A4  Ea4  \\\n",
       "214034           273    0.023977  125    0.062190  149   0    0   0    0   \n",
       "214035           273    0.053240   56    9.835595   74   0    0   0    0   \n",
       "214036           273    0.021213   14  134.300199    6   0    0   0    0   \n",
       "214037           273    1.815907  161    0.015825   84   0    0   0    0   \n",
       "214038           273    3.325518   25    1.991739   49   0    0   0    0   \n",
       "...              ...         ...  ...         ...  ...  ..  ...  ..  ...   \n",
       "1768531          273    3.648937   58    1.190083   21   0    0   0    0   \n",
       "1768532          273    9.533488  156  845.269909   44   0    0   0    0   \n",
       "1768533          273  823.826795   56    0.091007  118   0    0   0    0   \n",
       "1768534          273  786.118977   89    3.026795   12   0    0   0    0   \n",
       "1768535          273  546.467301  157  449.655166  165   0    0   0    0   \n",
       "\n",
       "         cSM_0s  ...  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  \\\n",
       "214034      0.3  ...           0.0           0.0          0            0   \n",
       "214035      0.3  ...           0.0           0.0          0            0   \n",
       "214036      0.3  ...           0.0           0.0          0            0   \n",
       "214037      0.3  ...           0.0           0.0          0            1   \n",
       "214038      0.3  ...           0.0           0.0          0            1   \n",
       "...         ...  ...           ...           ...        ...          ...   \n",
       "1768531     0.3  ...           0.0           0.0          0            1   \n",
       "1768532     0.3  ...           0.0           0.0          0            1   \n",
       "1768533     0.3  ...           0.0           0.0          1            0   \n",
       "1768534     0.3  ...           0.0           0.0          1            0   \n",
       "1768535     0.3  ...           0.0           0.0          1            0   \n",
       "\n",
       "         Slow_rxn1  Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "214034           1          0            0          1   \n",
       "214035           1          0            1          0   \n",
       "214036           1          1            0          0   \n",
       "214037           0          0            0          1   \n",
       "214038           0          0            1          0   \n",
       "...            ...        ...          ...        ...   \n",
       "1768531          0          0            1          0   \n",
       "1768532          0          1            0          0   \n",
       "1768533          0          0            0          1   \n",
       "1768534          0          0            1          0   \n",
       "1768535          0          1            0          0   \n",
       "\n",
       "                                                                 Reaction_order  \\\n",
       "214034   {'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}   \n",
       "214035   {'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}   \n",
       "214036   {'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}   \n",
       "214037   {'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}   \n",
       "214038   {'rxn1': {'SM': 1, 'B': 1, 'R': 1}, 'rxn_2': {'P': 1, 'B': 1, 'R': 1}}   \n",
       "...                                                                         ...   \n",
       "1768531  {'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}   \n",
       "1768532  {'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}   \n",
       "1768533  {'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}   \n",
       "1768534  {'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}   \n",
       "1768535  {'rxn1': {'SM': 2, 'B': 2, 'R': 2}, 'rxn_2': {'P': 2, 'B': 2, 'R': 2}}   \n",
       "\n",
       "         Mechanism  \n",
       "214034   (29, 217)  \n",
       "214035   (29, 217)  \n",
       "214036   (29, 217)  \n",
       "214037   (29, 217)  \n",
       "214038   (29, 217)  \n",
       "...            ...  \n",
       "1768531  (29, 217)  \n",
       "1768532  (29, 217)  \n",
       "1768533  (29, 217)  \n",
       "1768534  (29, 217)  \n",
       "1768535  (29, 217)  \n",
       "\n",
       "[5183 rows x 257 columns]"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_mechanism_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
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       "      <th>cSM_180s</th>\n",
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       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214034</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333088</td>\n",
       "      <td>0.330899</td>\n",
       "      <td>0.290399</td>\n",
       "      <td>0.257581</td>\n",
       "      <td>0.232218</td>\n",
       "      <td>0.183601</td>\n",
       "      <td>1.569823e-01</td>\n",
       "      <td>1.409035e-01</td>\n",
       "      <td>1.305241e-01</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>214035</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.332773</td>\n",
       "      <td>0.327830</td>\n",
       "      <td>0.255848</td>\n",
       "      <td>0.215770</td>\n",
       "      <td>0.191767</td>\n",
       "      <td>0.157620</td>\n",
       "      <td>1.444961e-01</td>\n",
       "      <td>1.386091e-01</td>\n",
       "      <td>1.357871e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214036</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333106</td>\n",
       "      <td>0.331077</td>\n",
       "      <td>0.294830</td>\n",
       "      <td>0.266705</td>\n",
       "      <td>0.245338</td>\n",
       "      <td>0.204300</td>\n",
       "      <td>1.812328e-01</td>\n",
       "      <td>1.668942e-01</td>\n",
       "      <td>1.574174e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214037</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.315938</td>\n",
       "      <td>0.211818</td>\n",
       "      <td>0.011282</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>5.826323e-07</td>\n",
       "      <td>4.411452e-08</td>\n",
       "      <td>7.773798e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>214038</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.301000</td>\n",
       "      <td>0.159667</td>\n",
       "      <td>0.059132</td>\n",
       "      <td>0.059105</td>\n",
       "      <td>0.059105</td>\n",
       "      <td>0.059105</td>\n",
       "      <td>5.910544e-02</td>\n",
       "      <td>5.910544e-02</td>\n",
       "      <td>5.910544e-02</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>1768531</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.326183</td>\n",
       "      <td>0.280727</td>\n",
       "      <td>0.132799</td>\n",
       "      <td>0.105015</td>\n",
       "      <td>0.091402</td>\n",
       "      <td>0.072409</td>\n",
       "      <td>6.354946e-02</td>\n",
       "      <td>5.814510e-02</td>\n",
       "      <td>5.440707e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768532</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.316506</td>\n",
       "      <td>0.243543</td>\n",
       "      <td>0.128805</td>\n",
       "      <td>0.111924</td>\n",
       "      <td>0.103662</td>\n",
       "      <td>0.091990</td>\n",
       "      <td>8.644600e-02</td>\n",
       "      <td>8.302818e-02</td>\n",
       "      <td>8.065024e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768533</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.125787</td>\n",
       "      <td>0.049262</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.005506</td>\n",
       "      <td>0.004032</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>1.629541e-03</td>\n",
       "      <td>1.271392e-03</td>\n",
       "      <td>1.046867e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768534</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.128569</td>\n",
       "      <td>0.050851</td>\n",
       "      <td>0.010255</td>\n",
       "      <td>0.006724</td>\n",
       "      <td>0.005265</td>\n",
       "      <td>0.003538</td>\n",
       "      <td>2.859491e-03</td>\n",
       "      <td>2.487114e-03</td>\n",
       "      <td>2.248223e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768535</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.149346</td>\n",
       "      <td>0.074596</td>\n",
       "      <td>0.041673</td>\n",
       "      <td>0.038872</td>\n",
       "      <td>0.037693</td>\n",
       "      <td>0.036276</td>\n",
       "      <td>3.571724e-02</td>\n",
       "      <td>3.541275e-02</td>\n",
       "      <td>3.521984e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5183 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "214034   0.333333  0.333088  0.330899  0.290399  0.257581  0.232218  0.183601   \n",
       "214035   0.333333  0.332773  0.327830  0.255848  0.215770  0.191767  0.157620   \n",
       "214036   0.333333  0.333106  0.331077  0.294830  0.266705  0.245338  0.204300   \n",
       "214037   0.333333  0.315938  0.211818  0.011282  0.002051  0.000473  0.000012   \n",
       "214038   0.333333  0.301000  0.159667  0.059132  0.059105  0.059105  0.059105   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "1768531  0.333333  0.326183  0.280727  0.132799  0.105015  0.091402  0.072409   \n",
       "1768532  0.333333  0.316506  0.243543  0.128805  0.111924  0.103662  0.091990   \n",
       "1768533  0.333333  0.125787  0.049262  0.009066  0.005506  0.004032  0.002297   \n",
       "1768534  0.333333  0.128569  0.050851  0.010255  0.006724  0.005265  0.003538   \n",
       "1768535  0.333333  0.149346  0.074596  0.041673  0.038872  0.037693  0.036276   \n",
       "\n",
       "             cSM_180s      cSM_240s      cSM_300s  ...  cINT1_2400s  \\\n",
       "214034   1.569823e-01  1.409035e-01  1.305241e-01  ...          0.0   \n",
       "214035   1.444961e-01  1.386091e-01  1.357871e-01  ...          0.0   \n",
       "214036   1.812328e-01  1.668942e-01  1.574174e-01  ...          0.0   \n",
       "214037   5.826323e-07  4.411452e-08  7.773798e-09  ...          0.0   \n",
       "214038   5.910544e-02  5.910544e-02  5.910544e-02  ...          0.0   \n",
       "...               ...           ...           ...  ...          ...   \n",
       "1768531  6.354946e-02  5.814510e-02  5.440707e-02  ...          0.0   \n",
       "1768532  8.644600e-02  8.302818e-02  8.065024e-02  ...          0.0   \n",
       "1768533  1.629541e-03  1.271392e-03  1.046867e-03  ...          0.0   \n",
       "1768534  2.859491e-03  2.487114e-03  2.248223e-03  ...          0.0   \n",
       "1768535  3.571724e-02  3.541275e-02  3.521984e-02  ...          0.0   \n",
       "\n",
       "         cINT1_3000s  cINT1_3600s  cINT1_4500s  cINT1_5400s  cINT1_6300s  \\\n",
       "214034           0.0          0.0          0.0          0.0          0.0   \n",
       "214035           0.0          0.0          0.0          0.0          0.0   \n",
       "214036           0.0          0.0          0.0          0.0          0.0   \n",
       "214037           0.0          0.0          0.0          0.0          0.0   \n",
       "214038           0.0          0.0          0.0          0.0          0.0   \n",
       "...              ...          ...          ...          ...          ...   \n",
       "1768531          0.0          0.0          0.0          0.0          0.0   \n",
       "1768532          0.0          0.0          0.0          0.0          0.0   \n",
       "1768533          0.0          0.0          0.0          0.0          0.0   \n",
       "1768534          0.0          0.0          0.0          0.0          0.0   \n",
       "1768535          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "         cINT1_7200s  cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "214034           0.0          0.0           0.0           0.0  \n",
       "214035           0.0          0.0           0.0           0.0  \n",
       "214036           0.0          0.0           0.0           0.0  \n",
       "214037           0.0          0.0           0.0           0.0  \n",
       "214038           0.0          0.0           0.0           0.0  \n",
       "...              ...          ...           ...           ...  \n",
       "1768531          0.0          0.0           0.0           0.0  \n",
       "1768532          0.0          0.0           0.0           0.0  \n",
       "1768533          0.0          0.0           0.0           0.0  \n",
       "1768534          0.0          0.0           0.0           0.0  \n",
       "1768535          0.0          0.0           0.0           0.0  \n",
       "\n",
       "[5183 rows x 240 columns]"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conc_list = [col for col in true_mechanism_df.columns if col.endswith('s')]\n",
    "true_mechanism_x = true_mechanism_df[conc_list]\n",
    "scaler = MinMaxScaler()\n",
    "true_mechanism_x_transposed = true_mechanism_x.T\n",
    "scaled_data = scaler.fit_transform(true_mechanism_x_transposed)\n",
    "true_mechanism_x = pd.DataFrame(scaled_data.T, columns=true_mechanism_x.columns, index=true_mechanism_x.index)\n",
    "true_mechanism_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "unrelated_groups = [group for group in range(1, 457) if group not in grouping]\n",
    "# print(unrelated_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[179, 183, 154, 178, 156, 191, 344, 177, 182, 342, 456, 341, 147, 155, 152, 153, 181, 150, 5, 151, 176, 452, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 180, 184, 185, 186, 187, 188, 189, 190, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 343, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 454, 455]]\n"
     ]
    }
   ],
   "source": [
    "all_groups = [179, 183, 154, 178, 156, 191, 344, 177, 182, 342, 456, 341, 147, 155, 152, 153, 181, 150, 5, 151, 176, 452, unrelated_groups]\n",
    "print(all_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29, 217)\n",
      "(29, 448)\n",
      "(17, 200)\n",
      "(29, 216)\n",
      "(17, 208)\n",
      "(33, 171)\n",
      "(7, 382)\n",
      "(29, 179)\n",
      "(29, 400)\n",
      "(7, 163)\n",
      "(97, 452)\n",
      "(7, 162)\n",
      "(16, 201)\n",
      "(17, 201)\n",
      "(17, 154)\n",
      "(17, 155)\n",
      "(29, 382)\n",
      "(17, 146)\n",
      "(1, 146)\n",
      "(17, 147)\n",
      "(29, 163)\n",
      "(97, 221)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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Usn9ATzzxhN797nc7Wosd2Mx2OrRMswObxBmXgHyRscDm6NGj+vCHPzyr0MU0zZxNuRUOh/WRj3xEknTRRRdp8+bNU7ZMlpWlvu5LuuzfhSnn55kEAAAAAAAAJKsr4cCBA5Kk5tX1zhYzXsPZl7u0Z88evf/973e0lscff1yS1V3j9bkdrWXZRfV6ZstpPf300xoeHlZlZaVjtWSqwyZT7OCIKdGQrzLyl7J3715deumleuyxx2Sa5rT/bIk/58Kjjz6qw4cPS5L++q//Oi/mt0wU7fbJg8DGsGpwIrgCAAAAAABA/njuuees9UPmVqii3ud0OWoe7/LZvXt3zscYE23dulWStOziekfrkKSGReWqaS5TIBCIrKvjlEhgkycdNmWsYYM8l/ZfSjAY1K233qr+/n6Zpqk3v/nNeuihh/S2t71NktUtsnXrVv3yl7/UAw88oNtuu01er9c6uFdX6zvf+Y62bt0aSaGz7b/+678idd18882R+7u7u3X48GF1d3fnpI6p2IFNPnTY2DUkThkHAAAAAACA0rJr1y5J6U+HZs8uk+6MO43La+X2utTd3a3XXnstrW2lw14PW5KWXeTs2j6S9Xu1g6NcjblOZnR0VJLkKcuPpR/KfFYdIyMjDlcCJJd2YPPDH/5Qx48fl2EYes973qPf/OY3uu2227Ro0aLIc6699lrdfPPN+uhHP6qHHnpIR48e1Vve8hYNDg7qrrvuks/n07XXXptuKTPyzDPPSJKWL1+umpoa/fjHP9b69evV1NSkNWvWqKmpSWvXrtWXv/xl+f3+nNSUKBrYOL9ujF0DgQ0AAAAAAEBpe/bZZyVJc9Y2zHobhmHo9ttv16OPPqrbb79dhmFopHd2Y3Bur0tN51gBiR0mOWHr1q0Kh8NqXlap6ibnO48kafkl9ZKstbxDoZBjdZSXl0uSQmPOX5guSWMBq46KigqHKwGSSzuw+fWvfy1J8ng8+vKXvzyj1yxcuFC//OUv9da3vlX9/f2644471NfXl24p0wqHw9F5Npub9Zd/+Zd617vepVdeeSXueYcOHdJdd92lN77xjert7Z10e36/X/39/XH/MqGmpsaq13DuYGoLy6qhtrbW4UoAAAAAAADglKGhocgYWjodNqZp6s4779TKlSt15513yjRNDXeNznp7di1OBjZ/+MMfJEVDknyw8NwalVW41dXVpRdffNGxOuz1cwKj+RHYBPzWxelOrusDTCXtwOaFF16QYRjauHGjmpubZ75jl0vf+c53VFZWplOnTukHP/hBuqVMq6+vT+Gw9Uf58ssv6xvf+IYWLFigH/3oR+ru7tbw8LCeeOIJvf71r5ckPf300/rABz4w6fbuvfde1dXVRf4tWbIkI3XW19dLksIay8j20hEyrBrq6pxv5wQAAAAwO/39/WptbXW6DABAATt58qRCoZB81V5VNc++O8EwDG3evFlHjx7V5s2bZRiGKpvKZ729xhXWRcZHjx6d9TbSMTQ0pKeeekqStPzSekdqSMbtcWnphdZ43u9+9zvH6ogGNs7PJCRJ/vHgiMAG+SrtwKazs1OStGrVqrj73W535PZkcwLOmzdP11xzjUzT1E9/+tN0S5nW0NBQ5Pbo6KgqKyu1detWvetd71JDQ4MqKip0zTXX6PHHH9dFF10kSfr5z38eafdM9OlPf1p9fX2Rf6dOncpInXZgY4clTrJDI7smAAAAAIXn1ltv1Q033DDlDAIAAEzl7NmzkpRWuCJZHTZbtmzRjTfeqC1btsg0TVXUz34asYpGqx6nLkx49NFHNTo6qrp5PjUvza8Q4JyNjZKsGZKcmhatqalJktTXFZRpOt9l098VlBStC8g3aQc2gUBA0sRU0p7WS5La29snff3y5csl5SYFt+dMtP35n/+51q5dO+F5FRUV+od/+IfIzw899FDS7fl8PtXW1sb9y4SGBmse0HzosAkbBDYAAABAoTt+/LhGRka0f/9+p0sBABQoOxCpaEh/jRZ74D4TA/iVjVY9PT09kQXuc+kXv/iFJGnNFU0yDCPn+5/Ksovq5Ktyq62tbdIL0rNt5cqVMgxDo0NhDfU7v/xD5xlrLDux+QDIF2kHNna4ENu9Iklz5syJ3D506NCkr7fDHLtTJ5tiQyRJuvHGGyd97vXXXy+PxyNJ2r17d1brSmT/TvOhwyYkK3VubGx0uBIAAAAAAAA4pa2tTZLS6obJBm+FR+4ya4gz11027e3t2rlzpyQrsMk3bq8r0mXz8MMPO1JDeXl5ZBmJzpaAIzXEsmsgsEG+SjuwWb16tUzT1OnTp+PuX79+feT2o48+mvS1o6OjkTAkF4va+3y+uCBpqjVnysvLI2vydHR0ZL22WPnUYWOHRnZNAAAAAAAAKD32zDWhQH6sRWIzw6bCY1anTkXF7NfWmY2nn35a4XBYc1ZUqnZuelPFZcuq11mBzVNPPeXYlGTnnHOOpGh3i1PMsKnOs1YNdk1Avkk7sLn00kslSfv27Yu7//Wvf32ko+V73/ueDh48OOG1n/3sZ3X27FkZhqFLLrkk3VJm5Pzzz4/cnm7uRvtxu9MmV+xwxDTCCsvZVsEwgQ0AAAAAAEDJW7BggSRpuDv3045NZbQvIDNsyuv1xl2onQvPP/+8JGnh2pppnumcuSur5HIbamtr05kzZxypwV4So6c96Mj+bQO9YxoLmHK73Vq8eLGjtQCTSTuweeMb3yjJagF89dVXI/dXVFTo3e9+t0zT1MDAgDZu3KiPf/zj+t73vqevf/3ruv766/XVr3418vz3vve96ZYyI9dcc03k9lTr5vT390emaVu0aFHW64pVVVUlt9stSc4HNuNdPnV1dY7WAQAAAAAAAOcsXLhQUv4FNsNdVj3z58+Xy5X2UGdKnnvuOWvfq6tzut9UeH1uNS+z1h636801O7DpanW2w6a7zQqMFi9eLK/X62gtwGTSPor90R/9kaqqqmSapn7yk5/EPfbFL35RS5YskWmaGhwc1L/8y7/owx/+sD75yU9q27ZtkefdeOONeuc735luKTPyJ3/yJ5HbP//5zyd93s9//vNIm+DVV1+d9bpiGYYRmSIu7OA6NqbMSGBEYAMAAAAAAFC6IoFN16hCwfyZFm2wfURStAMoV0ZHRyPrds9blb+BjSTNH6/v5ZdfdmT/y5YtkxQNTJzSM75/ux4gH6Ud2JSXl+uxxx7TI488oltuuSXusYaGBm3fvl2bNm2SaZoT/knSe97zHv3sZz9Lt4wZu/DCC/XmN79ZkvSTn/xEjz322ITntLa26m//9m8lSWVlZXr/+9+fs/pskcDGwQ6bsEKSYd22p7cDAAAAAABA6Vm0aJGam5sVDpnqOtLrdDkR7Qd6JEkXX3xxbvfb3i7TNOX2Gqqsy+9ujdq5PknS2bNnHdm/HZD0dgRlhp1ZR0eSutusDp+lS5c6VgMwnYwszrJx48ZJH1u6dKl27NihnTt36rHHHlNLS4tcLpdWrlypm266SWvXrs1ECSm5//77tXPnTvX29urmm2/WX/3VX+ktb3mLKioqtGvXLt177706ffq0JKtLKNdToklSU1OTTpw4oTHDL5nOhCUhwzqIVVdXy+fzOVIDAAAAAAAAnGcYhq644gr98pe/VNurPZp7bqPTJck0TbXt75YkXXnllTndd0dHhySpsr5MhmHkdN+pqqy3AiW75lxbsGCB3G63QsGQBnrHVNvoTMDV22F12BDYIJ9lJLCZiU2bNmnTpk252t2U1qxZo1/96ld6+9vfrra2Nv3TP/2T/umf/inuOYZh6P/7//4/3X333Y7UeN555+n555+X3xhStZodqcFvDEVqAQAAAAAAQGm76qqrxgObbq2/9Ryny9HA2WGN9PhVVlamyy67LKf7bm9vlyRV5Xl3jaRIB5BTgY3H49HChQt16tQp9bQHHQtsetqtwGbJkiWO7B+YidyuxJVHrrrqKu3bt0+f+9zndNFFF6m2tlbl5eVasWKF3v/+9+u5557TF7/4Rcfqu+CCCyRJAdeQYzXY+7ZrAQAAAAAAQOm64oorJEndx/vlH3R2PRJJat3XJUnasGGDysvLc7rv0dFRSZLHl//Dq3aNfr/fsRrsrpbuVmfeN6ZpRtbQYQ0b5LO0O2w+8IEPSJLe9KY36Z3vfGfKr9+yZYv+53/+R4ZhaPPmzemWk5KmpiZ9/vOf1+c///mc7ncm1q9fL8nqcjEVluFAtuY3BuNqAQAAAFDY7LVEAQCYjfnz52vt2rU6ePCg2vZ1aenr5jtaz9mXrcDm6quvzvm+y8rKJEnhUP5/tobHrBrtmp2wdu1a7dixQ20nnQmN+jrH5B8Oy+v1avny5Y7UAMxE2inAgw8+qB/84Ad65plnZvX63bt368EHH9SDDz6YbilFZdWqVWpoaJBphDU6HpzkUkjByJRol19+ec73DwAAACDz8n2OfQBA/rv22mslSWdf6nK0jjF/SB0HeyVJ1113Xc73b4cfoWA4o9u1P6sz+ZkdCjof2Kxbt06SHAtsWk9Y+121apWjvwdgOvnfs1ei3G633vCGN0iShl09Od//sKtXMqzp0BYsWJDz/QMAAAAAACD/2IFN6ytdCoed6y5pP9Cj8FhYixYt0jnn5H49nYqKCklSYCSUsW0ahqHbb79djz76qG6//XYZhqHh3kDa2w2MWjX6fL60tzVb9hrZrSf8Mh1437SeGI2rA8hXjgc2dks+V3pNdP3110uSht09MpXbA5kdEtk1AAAAAAAAAJdeeqmqqqrkHwyq73TuZ4Wxtb3aLUm65pprHBlXXLFihSSpr82v0FhmumxM09Sdd96plStX6s4775RpmhroTD+w6TkzIklauXJl2tuarZUrV6qyslKB0bDaz6T//ylVpw9bgQ1rdSPfOR7YdHZ2SpKqq6sdriT/XHXVVSorK9OY4VfAGM7ZfsMa04irVxKBDQAAAFBMWMMGAJAur9cbmT6/42DuZ4Wx2ft+/etf78j+Fy5cqKqqKoVDpvraMjPNl73G99GjR7V582YZhqGa5vSn7+oeD2xWr16d9rZmy+v16tJLL5Ukndifu3FOSQqNmTp12PodvO51r8vpvoFUORrY9Pf369FHH5VhGFq6dKmTpeSlqqqqSGAy6OrM2X6HXN0yDVPnnHNOZH5JAAAAAIWPmQ0AAJmwceNGSYqsIZNrgaGgese7e+xacs0wDK1atUqS1HkyMwGEaZrasmWLbrzxRm3ZskWmaaqyPv3ApuuUVZ+TgY0UDUtO7B/J6X5bjo5qLGCqoaHB8d8BMB1PKk/+whe+MOlju3btmvLxWMFgUGfOnNGjjz6q1tZWGYahq666KpVSSsatt96qRx55RIPuTjWGlsjIQcY24O6M7JsTOgAAAAAAAMSKBDaHemSGTRmu3I4fdR7ulUxrWrI5c+bkdN+xNm7cqBdffFGvPdOtNZuaMrJNuxs2U12x3WdG1HVyRG63O9Lh4hQ7sDm+f1jhsClXjt43x/ZZgdXGjRsZ60TeSymw+fznP5/0TW2apnbv3q3du3fPqoiysjJ99KMfndVri91VV12lpqYmdXV1acTVp8pwQ1b3F5RffteADMPQW9/61qzuCwAAAAAAAIXnggsuUFVVlYaGhtR7elANS2tyuv+2A9Z0aE5Pb/X2t79d3/ve93TyxV4NdgdU3Zh+N0ymHXiyQ5L0hje8wdFwS5IuvPBC1dXVqa+vT2deG9WSNRU52e/hvUOSrPWOgHyXcruGaZpx/ya7f6b/lixZop/+9Kc6//zzM/p/rFh4vV695S1vkSQNubI/L+iw21qwbcOGDVqwYEHW9wcAAAAgd1jDBgCQCV6vVxs2bJAkte/vzvn+21+19rlp06ac7zvWOeeco8svv1ymKR18KnfLGcxUaCysgzu6JEm33367w9VIHo9HV199taRoiJJtQ31jajk2Kkm67rrrcrJPIB0pddh87nOfm3Df3/3d38kwDG3YsEFvfvObp92GYRgqLy9XU1OTLrjgAlrRZuBNb3qTfvjDH2rY1SNTpgxl7/c1PB4K3XDDDVnbBwAAwEyZpsl3RSCD+HsCAGTKpk2btG3bNrXt79HaP1qWs/2O9vnVd2YoUoPTbr/9du3Zs0evbu3QxW+ZL7fH0SXD4xx5tlujA2OaN29e3nSXXHfddfr1r3+tw3sH9cbbm7O+v9deHpZM6bzzztO8efOyvj8gXRkJbCRrDsBkjyN9GzduVE1NjQYGBuQ3BlVuZqfNNKQxjRoDkqyQCAAAwEktLS267bbb9Gd/9mf6yEc+4nQ5AAAAiHHFFVdIkjoP9So8FpYrR0FF+/h0aOvWrVNjY2NO9jmVm266Sffdd5+6urp0dE+PVr8+M2vZpMs0Tb34uzZJ0rve9S55PCkNA2eN3WHTfiqgwb4xVddlt66jLzMdGgpL2kfSa665Rtdcc41Wr16diXqQhNfr1bXXXitJGnH1ZW0/o64+yZDWrFmjJUuWZG0/AAAAM/G9731PbW1t+spXvuJ0KQAAAEiwdu1a1dfXa8wfUs/JgZztt+NQryTn16+x+Xw+/dmf/Zkk6cX/ac2b6UdbDgyo88SwKioq9M53vtPpciKampq0bt06SdLxV4ezui/TNHV8n7WPK6+8Mqv7AjIl7cBm27Zt2rp1qz72sY9loh5M4rLLLpMk+Y2p53f0qEyL/RdrkX995L5F/vVa7L9YHk298Jm97csvvzzNagEAANI3MJC7E38AAACkxuVyRcaQ7BAlF/ItsJGkd77znSorK1PHsWG1vTbodDmSpJcetbprbr31VtXX1ztbTAI7PDn2SnYDm44zAQ32hVReXq5LL700q/sCMiV/JlXElNavtwIYv2tQpiZP6g0Z8sonj3yR+zzyySvftGvf+F1WYHPBBRdkoGIAAAAAAAAUsw0bNkjKXWDjHwiov8Uav7Ivbs4HTU1NuuWWWyRJ+5/sdLgaabg3qBN7eyVJ7373u50tJgl77aGj+4az2pF0bLy75rLLLpPP55vm2UB+yGlg87Of/Uzve9/79OY3v1nvfe979Z//+Z+53H1BW7dunTwej8LGmEIKZHz7pkwFxjts7HAIAAAAAAAAmIwd2HQd7s3JVGBdR6ylAlatWpUX69fE+pM/+RNJ0pFd3Qr6Q47WcujpTplh6eKLL9aqVascrSWZjRs3yuv1qr9rTF2twUmfV9vo0ce+ukIfvndZ5L4P37tMH/vqCtU2Tr/2zdGXrcDGXjcHKARpBzZtbW2RdWw+85nPJH1OOBzWrbfeqttuu00//OEP9eijj+pHP/qR3vWud+kNb3iDhoamnuYL1nyYa9eulSSNujLfWhk0RhU2rBbBfDyQAwAAAAAAIL+sXbtWhmEoMDym0f7MX2CcqG+8u+a8887L+r5StWHDBi1btkzB0bCO7u5xrA7TNHVgu9Xl8/a3v92xOqZSUVERCfuOvjz5uLDLbah+jld1zd7IfXXNXtXP8crlnnomobFgWCf2W4HNVVddlYGqgdxIO7D5+c9/rqeeeko7duyYtBXxn//5n/WLX/xCpmlO+Pfkk0/qzjvvTLeMknDJJZdIkvxG5gMbv2HNEb9+/Xp5vd5png0AAAAAAIBS5/P5tHjxYknSQGt21yOJ3ceKFSuyvq9UGYYRmRbt9L5+x+oY7g2qp2VULpdLN910k2N1TMfuerG7YDLt9OFRBQOm5syZE7kIHigEaQc227dvlySVlZXpzW9+84THA4GAvvSlL8kwDBmGoRtuuEHf+MY3dNddd6m8vFymaeq//uu/tGfPnnRLKXqRwMaV+QV47a4dex8AAAAAAADAdFauXCkpR4FN23DcPvPN+eefL0nqPjPiWA32vpctW6aamhrH6pjOFVdcIUk6eXBE4XDmp9M7Pt5ds2nTJhnG1N04QD6ZfrK/aezbt0+StVB9ZWXlhMcfeeQR9fT0yDAMvfWtb9XPf/7zyGOXXnqp3vGOd0iSfvSjH+nyyy9Pt5yidvHFF0uS/MawTIVlZHAJInv9GgIbAAAAAAAAzNSKFSv0xBNPaLAt+4HNYHv+dthI0urVqyVJPS1WCOFy5T4osAMbu5Z8de6556q6ulqDg4NqO+nXguXlGd3+yQPW72Hjxo0Z3S6QbWmP+J8+fVqGYWjNmjVJH//9738fuX3XXXfFPXbbbbdp4cKFkqRnnnkm3VKK3tKlS1VdXS0ZpoLGaMa2ayqsgGEdxOwrAQAAAAAUn1wsCA0AKC1z586VJI32ZXcNm3AoLP+AtUD9vHnzsrqv2VqyZIl8Pp9CQVODnX5Haug9a40Z5vsa1W63O3Lxvh2uZMpYMKzTR6zfA4ENCk3agc3AgDU9V11dXdLH7SnTmpubI61ukZ27XLrssstkmqaOHDmSbilFzzCMyJyLASNzVy0EjVHJMFVTU6MFCxZkbLsAAAAA8gtTggAAMm3OnDmSpNH+7AY2/oGgZFoD/fX19Vnd12zF1hYYCTtSQ2A4JElqampyZP+psAObU4cyG9i0nvArFDTV2NiYt91YwGTSDmzsL/xjY2MTHuvr69O+fftkGIauuuqqpK+3E/H+fucW4yok2Qhs7G2tXbuWEzgAAAAAAADMWHNzs6TsBzZ2B09jY6PcbndW95WO8nJraq+gP+TI/oP+cFwd+cye6aftVGa7kdpO+CPbZ6wThSbtNWzq6+vV0dGh06dPT3jsySefVDgclmEYuvLKK5O+3g568vlAm0/sdsZMTolmT4eW73NbAgAAAAAAIL/YHTYjvVMPulc0+HTTfVdoLBDS7z77rCTpj774OnnK3Kpo8E27H3v7dkCUryoqKiRJYwFnOmzs/dp15LN169ZJkrrbggqMhlVWnpn1uu0AyN4+UEjS/itYu3atTNPUzp07NToaHyL89Kc/jdy++uqrk76+tbVVUmG06eWD5cuXS8psYDM2vq1ly5ZlbJsAAAAA8gPr1gAAsmn+/PmSpMBgUKHg5F0lLrdLVc0VqmqKBglVTRWqaq6Qyz39EKUd2Nj7y1dVVVWSpMCIQx02o6G4OvJZc3OzFcCZUsfpzHXZtJ20tnXuuedmbJtArqQd2Fx//fWSrCnN/uZv/iZy/9NPP62HHnpIhmFo3rx5kTkJE+3du1eGYeicc85Jt5SSYIcqY8aoTGXmxCto+OO2DQAAAKB4ENgAALKprq4uMv3WSE9mp7aKNdJjXXCc7+sv2+t8+4ecCWz8Q9ZsRvm6zk+iNWvWSJI6WzIzpZ5pmuo8E4jbNlBI0g5sPvCBD0Ra7B544AEtX75cl112md7whjcoELD+OD74wQ8mnS9w//79kQ6bSy65JN1SSsKiRYvk9XplGqbGlP6BzJSp4PiUaHb3DgAAAIDiQWADAMgm+2JtSRrOYmAz3F0YHTZ2UGIHJ7k2Omjt1w6O8t2KFSskSZ1nMxPYjAyGNTpsTQvHWCcKUdqBzeLFi/W1r30tchJw6tQp7d27V8FgUJK1Lspdd92V9LVbtmyJ3L7qqqvSLaUkeDyeSDoccA2mvb2gMSrTCKu8vDxygAQAAABQPMLh6Bz6hDcAgGxYvHixJGmoYyRr+7C3vXDhwqztIxPsoGRkIPeBTThkyj9sdfYUSoeNPR7Z1RrMyPa6xoOfhQsXRjq/gEKSkZWcPvShD+lnP/uZLrroIpmmKdM0VV5erjvuuENPPPGEqqurJ7wmGAzqu9/9riQrhHjTm96UiVJKwsUXXyxJGjXSD2z849tYv369PB5P2tsrFN3d3U6XAAAliYFCAHBWspkPAABIlz3oPtA6nLV92NvO92UV7A6goe7MdIykYqgnIJmS1+tVY2Njzvc/G5EOmwxNiWZvhwvTUagyEthI0tve9jY9//zzGhgY0JkzZ9TX16cf//jHkZbIRH6/Xz/+8Y+1detWbd++vWDa9PKBHdj4M9BhY2+jlKak+/Wvf62NGzfq3//9350uBQBKSn9/v6677jp94QtfcLoUACgphOUAgGxbuXKlJKn/7FBWth8YDmq03xqIz/dprhYtWiRJGujM3vRwkxnosn5HCxYskMuVsWHfrFq7dq0kqftsQMFAeJpnT6/tpD9uu0ChyfhfblVVlRYsWCCv1zvl86qrq3Xttdfq2muv1caNGzNdRlGzw5WAMSRT6R3I7A6biy66KO26CsU999wjSQwYAkCO/eIXv9CZM2cIzAEAAIAiY3e9ZKvDxt7uvHnzVFNTk5V9ZEoksOnKfYfN4HhIZNdQCObNm6eGhgaZptRxOv3fmR3YrFu3Lu1tAU4ojKgVcZYtW6b6+nqZhqmAMfsPwrBCkdfbXTuloL+/3+kSAKAkBQK5P2EBANBhAwDIPjuwGWwfUSiYfpdEov4Wq3PH7uTJZ0uXLpUkDfcG5R/O7To2PWdH42ooBIZhRMIVO2yZLdM01XaKwAaFjcCmABmGEV3HJo1p0fzGoGRYi3BNNnUdAAAAAAAAMBW788UMmxpozfy0aH1nrG2uWbMm49vOtLq6usg4W/fpkZzu295foU0Hdt5550mSzh4bTWs73W1B+YfDKisry/u1joDJENgUKHtaNHtKs9mw168ppenQAAAAgFJDhw0AINsMw4iEKXY3TCb1t1hjWKtXr874trMhsi7LmdwGNl3jgU0hBFuxLrzwQklSS5qBTctR6/XnnXfetMt1APnKM5MnxbYbGoahI0eOJH0sHYnbxdTOPfdcSVLQmP2BPzD+WjvFBgAAAFB8CGwAALmwZs0aPffcc+o9Pailr8vstgupw0ayApsnn3xSXadyF9gERkIa6PBH9l9I7MCm7aRfY4GwPGWz6zE4Ox7Y2NsDCtGMApvjx4/LMAyZpinDMJI+lo5k28XU7La+oDEiU6YMpf77s8OeVatWZbQ2AAAAAPmDwAYAkAt2mNJ3evazwSTjHwhopMcft498F+mwOTX7tadTZXfzzJs3Tw0NDTnbbyYsWrRIDQ0N6unpUfvpgBauLJ/VdlqOWe+T9evXZ7I8IKdmHFdO9SXfNM20/iF1S5YsUVlZmUzD1JhSX5DLlKmgYaXOzOkIAADyEd8TgcwIhzO/+DMAAInsGVx6T2U2sLG3t3TpUlVXV2d029liz4zTdXokZ99pu8bDoULrrpGsmZfs90/bydTHOSXr3KH9lPVaZhNCIZtRh82xY8dm9Riyx+12a8WKFTp48KCCrhF5w6klzyEFZBpheb1eLV26NEtVAgAAAHAa4ScAIBfsoGCkxy//QEC+mrKMbNcObNatW5eR7eXCypUr5fF4FBge02BXQDXNvqzv055+zQ6LCs3atWu1Y8eOSOiSqr6uMflHrLHOFStWZLg6IHdmFNgsW7ZsVo8huyKBjZH6glz2a5YuXSqPZ0ZvAwAAAAAFiMAGAJAL1dXVWrp0qU6ePKmekwOaf35TRrbbe3JAUmEFNmVlZVqxYoUOHz6snpbRnAQ2PeNToq1evTrr+8oG+7/vbDts2sdft2rVKpWVZSYsBJwwuxWckBfstDidwGb58uWZLAkAAABAnokNbAhvAADZdMEFF0iSek4MZGyb3Sf6JRXeuiQrV66UJPW0jEz73OrGMr3ryxfqT//x/Mh9f/qP5+tdX75Q1Y0zCx96zxb20gd20NR5NjCr13e2WK9jrW4UOgKbApaJwIYWQQAAAKC4xa5hYxiGg5UAAIrdhRdeKEnqOd6fke0FR8Y00GqtzWKHQYXCDg56WqYft3O5DdXO8cV14tQ0+1Q7xyeXe/rPbv/QmIb7gpKiQVGhsWdxGuoLyT8SSvn13W1WYMPF6Sh0BDYFzE7Mg8b0SX2igGs4bhsAAAAAilNsYAMAQDbZoUr38cx02PScHJBMaeHChWpqyswUa7lij7nZU5VlU/f4PubPn6/q6uqs7y8bampqIv+Nu1uDKb/efg2BDQpdRhYv6e3t1VNPPaVnnnlGbW1t6unp0cDAgGpra9XY2Kj58+fr9a9/va666irV1NRkYpeQ1SpoGIZCCiqkoNzyzvi1AcMKbAp1ITIAAAAAMxM7DRrhDQAgm84//3y5XC4Nd41quHtUlY3laW2v83CvpGjnTiGxx9y6Tg/LDJsyXNnrcu06ZQU2hbTOTzIrVqxQV1eXOlsCWrAitfdO11k6bFAc0gpsnnrqKf3DP/yDfv/7389oLmSXy6U3v/nN+tu//Vtt3LgxnV1DUmVlpZYuXaoTJ04oYAyrwqyb0etCCipsjMkwDOZ1BAAAeYupm4DMiA1pWMMGAJBNNTU1Wr9+vV588UW17+/R8isXpLW9tle7JUmbNm3KRHk5tXLlSpWVlSkwGlB/h19189ILr6bSeaI4Lsxes2aN9uzZo/bT/pReN9Q3psG+kAzDiKyFAxSqWU2J5vf79Y53vEPXXnutHn30UYXD4cgXf9M0J/yz7w+FQvrNb36jTZs26f3vf7+CwdTb2xBvzZo1kqSAa+btlXZ3zdKlS1VRUZGVugAAAADkh9iQhsAGAJBtV155paRo2DJbY/6Quo70xW2zkHg8nsi4XefJ4azuq+tkcQQ2dv3tJ1MLbNpOWc9ftmyZKisrM14XkEspBzaDg4O69tprtWXLlglf9l0ul5YvX66LLrpIV155pS688EItW7ZMLlf8bkzT1L//+7/r+uuv18hI9udxLGZLly6VJI1p5geyMcMf91oAAAAAxSu2w4Yp0QAA2XbFFVdIktr2d6d1oUDn4V6Fx0wtXLgwsiB9obGncmt7bTBr+xgLhCOBUCFOHRfLDmzsAGam2k9Z06GtXbs24zUBuZZyYPPxj39cu3btivxcXV2tj33sY3ryySfV39+vo0eP6oUXXtD27du1d+9eHTt2TH19fdq2bZv+4i/+QlVVVZKs0GbHjh365Cc/mbn/NyVo0aJFkqIhzEyMGdZBbPHixVmpCQAAIBPoBAAygw4bAEAuXXLJJSovL9doX0D9LUOz3o7doXPFFVcU7FS5l1xyiSSpNYuBTcfxIYVDpubMmVPwY332dGYDPSH5R0Izfl1niz/u9UAhSymwefTRR/Xggw/KMAyZpqmbbrpJx48f19e//nVdddVVk06vVVlZqWuuuUbf/OY3dfz4cb3lLW+RZJ0sfPe739W2bdvS/j9SqpYsWSIptcAmaIxKIrABAABAcQkGg/rDH/6gnp4ep0vJKwQ2AIBc8vl8uuyyyyRJbftn/5lsv9bu2ClEl156qSSp4/iwxgLZ6XJtPTwY2VehBlu2mpoaNTc3S5K6zs58KQ37uStXrsxKXUAupRTYfOMb34jcvv322/WrX/1KDQ0NKe2wsbFRv/71r3XbbbdF7rv//vtT2gai7NAlaPhlamYnX3a4Q2ADAACAYvLf//3f+vCHP6x77rnH6VLyClOiAQByzV5zpn2W69j4BwLqPTUgSdq0aVPG6sq1pUuXqrm5WeExU+3HZt9tNJWzh6zfk93NU+hWrFghSeo6G5jxa+znEtigGMw4sDl16pT+53/+R4ZhaPHixfq3f/u3tHb8b//2b1qyZIlM09Rvf/tbtbS0pLW9UmWHLqYRUlgzaxVkDRsAgBO4qhup4j2DVP3oRz+SJD3yyCMOV5JfCGwAALlmd8W0H+xROJT6Z0/7wR7JlNasWaM5c+ZkurycMQxDGzdulCS1HBjI+PbDYVNnD1odNq9//eszvn0n2KFLV+vMApvRoZCG+q0x0eXLl2erLCBnZhzYPP7445Ev9x//+MdVWVmZ1o6rqqr0sY99TJIUCoX0hz/8Ia3tlaqKigrNnTtXkjQ2PtXZVMIKKWRYbYL2dGoAAAAAilds+ElgAwDIhXXr1qmqqkpjoyH1nUm9s6TztT5JioQdhSwa2PRnfNtdJ4YVGAmpurpa69aty/j2nRAJbGbYYWM/b968eaqurs5aXUCuzDiwee655yK33/3ud2dk57Hbid0+UmMHL8EZrGNjd9dUV1errq4uq3UBAAAAcB5r2AAAcs3tduviiy+WJHWNhy+psF9jrwFTyF73utdJstaaCY1l9sKJloNW186GDRvkdrszum2n2IFNZ8vMAptOpkNDkZlxYPPCCy9IkpYtWxbp6EjXvHnzIvMS2ttH6uypzWbSYWOHOsuWLSv4hcgAAAAATI/ABgDgBHtNla4jqQU2oWBIvSeLZ12WVatWqaGhQaGgqY7jwxnd9tnxwKYYOpFs0SnRggqHp//eYgc7BDYoFjMObNra2mQYhs4///yMFnDeeefJNE21trZmdLulxA5sZtZhMxr3GgAAAADFjSnRAABOsLtjuo6mFtj0HB9QOGSqubk5snZzITMMQ5dddpkkqfVQ5taxMU1TZw9b69fY2y8GixcvVllZmUJBU32dwWmfbwc255xzTrZLA3JixoFNf781z2J9fX1GC7C319eXenskLLPpsCGwAQAAAEoDIQ0AwAkXXnihJGmwfUT+wekH3m1dx6wxyIsvvrhoZoe5/PLLJUlnDw1mbJu9raMaHRiTz+fL+AX2TnK73ZEZmWYyLRqBDYrNjAMbO1DJ9LontbW1kqKBEFK3bNkySVJwBoGNHerYrwEAAACKBdN9JceUaAAAJ9TX10fGn3qOz3zcz36uHfgUA3vKspaDVvdQJpx51erWufjii+Xz+TKyzXxhhy8dZ6YObMaCYfW0BeNeAxS6GQc2fr/VmeFyzfglMytgfHuBwMwWksJEduocMoIKKzTlc4PGiCTmdQQAAPmvWK6oBPIJgQ0AIJcuuugiSVL3sZkHNvZz169fn5WanHD++eerrq5OgeGQ2o8NZWSbp16xLq6/6qqrMrK9fLJq1SpJUuc0gU13a1CmKVVXV2vevHm5KA3IusymL3BEXV2dGhsbJU3dZRNWWGOyDnR2yAMAAACguMVOiUZgAwDIJTt06Z5hh01geEyD7SNxry0GbrdbV1xxhSTp1MvpLwsRGgvrzKvW7/Tqq69Oe3v5JhLYTDMlWsf446tWreJiLxQNApsiYXfM2B00yYwZo5JhTUNnBzwAAAAAAABANpx77rmSpP6WmXWV9J+x1niZP39+xtfRdprdCXN6X/rLQnQcG1JwNKyGhgadd955aW8v39iBTceZwJQXm3Se8cc9HygGnlRfsGvXLn3hC1/IWAG7du3K2LZK2bJly7Rnzx4FDf+kzwnGrF9D6gwAAACUBtawAQA4Zc2aNZKkwY4RjflD8vjcUz6/bzywWbt2bdZry7XXv/71kqT2o0MK+kPyTvO7mErLAWv9mo0bN2Z8+Yp8sHz5crndbgVGQ+rvHlNFVfLflb3GDYENiknKgc3u3bu1e/fubNSCNNiLuI1NMSXa2HiYs3Tp0pzUBAAAAAAAgNLV1NSkpqYmdXV1qb9lSI0raqd8ft8ZqxPHDnqKydKlSzVv3jy1tbWp7ciQFp839e9iKi0Ho4FNMSorK9PixYt14sQJ9bQHVbEieWDT0xaUxNIPKC4pR7CmaWb8H9JnBzYz7bABAAAAUBpiz7li17MBACAX7PDF7p6Ziv2cYgxsDMOIBCx2h8xshEOmWg9bv6diDWwkafHixZKk3o7gpM+xH1uyZElOagJyYcYdNtdccw3TaOUxu2tmqg4bO7ChwwYAAADFiPOV5LhIDgDgpNWrV2vnzp3qPzv9Ojb2WjerV6/OdlmO2Lhxo371q1/p7MHZBzYdx631a+rq6opy6jibHcJMFtiMDoU0OmxdiLJo0aKc1QVk24wDm23btmWxDKTLPoiFjKDCCiV9DlOiAQAAAKWHNWwAAE6y1xexw5jJjA4E5B+wBudXrlyZ9bqc8LrXvU6S1HZkUGOBsDxlqa8/Y3fnbNiwoSjXr7FN12HT22nd39DQoKqqqpzVBWRb8f5Vl5j6+nrV1lpzX44lmRbNlKkxWffTJggAAIBiRBiRHFOiAQCcNNPAxn588eLFqqyszHpdTlixYoXmzJmjUNBU+9Hpp4hLxg5s7PCnWNmBTV/nWNLHezus+xnnRLEhsCki9gEq2To2Y/JLhrVo17x583JdGgAATFUEAA6hwwYA4CQ7sBnqHFVwNPnguyT1nynu6dAk65zIDlpOv5r6tGihsbDOHiqtwGbSDpvx++3nAcWCwKaI2PM1hpQksDECkqSFCxcWdbskAAAAgHixXTUENgCQW0ePHtW3vvUtDQ1Nv35LsWpsbFRzc7Okqbts+s5YHSfFHNhI0hVXXCFJOr2vL+XXth2x1q9pbGzUueeem+nS8op9YXp/z5hCoYnfX/o6CWxsAwMDOnHihNNl5KVAIOB0CSlj5L6ILFiwQFI0nIkVGr/Pfg4AZMJPf/pTPfnkk06XAQAApkCHDQA456677tJXv/pVff/733e6FEetXbtWktR3ZqrAZijuucXqqquukiS1HxmSf2jyjqNkTr9ihTxXXnll0V+Q3djYaE2NZ0bDmVg97dZ9TIkm3Xnnnbrhhht0/Phxp0vJK3v37tUll1yi733ve06XkpLi/ssuMVMFNvZ98+fPz2lNAIrXa6+9pr/5m7/RBz7wAadLAQBAEmHEZAhsAMA5L774oiTpsccec7gSZ9ldM32nk6/bYpqm+sc7bNasWZOzupywcOFCrVy5UqYpndmf2rRop/b1S7ICm2JnGIaWLl0qSeptnxjYdI/ft2zZspzWlY+ef/55hcNhPf74406Xklfuu+8++f1+3XfffU6XkhICmyIydWDjj3sOAKTr7NmzTpcAoMgxsAwAAFAc7HVs+s8m77AZ7Q8oMDwml8ullStX5rI0R2zatEmS1HJw5oFNcDSkjmNDca8vdnZg050Q2ITDZiTEsZ8DJBoYSH2dqHxAYFNE7O6ZULIp0cSUaAAAoLAYhuF0CSgwvGeSo8MGAOA0u8NmYJLAxl7bZsmSJfL5fDmryymXX365JOlsCoFN25FBmWFrbM9ex7rYRTpsOuIDm4HuMYXGTHm9XsY6UXQIbIpIpMNGAZmKPxEbYw0bAABQYBhYBjIjHA4nvQ0AQK6cc845kqThbr+CoxPXbbE7b+znFTs7sOk6OazASGhGrzl7yJoybsOGDVmrK9/YwVR/V3xg0zf+8/z58+V2u3NeF5BNBDZFZO7cudaCY4apkOIPZAQ2AAAAQGkisJlaIDBxhgIAQGbV19erqalJkjTYNjzh8YGz1n321GnFzu6SMU2p/VjyrqNEbUeswObSSy/NZml5xQ5s+rriQ77ezrG4x4FiQmBTRDwej+bMmSNJChnRwCaskMKGdSAjsAEAAABKC1OiTW7r1q266KKL9LOf/czpUgCg6Nlr0wy0jUx4bKDVCmxWrFiR05qcdP7550uyumxmonP8eRdccEHWaso3CxculCT1dyd02HQG4x4HigmBTZFJto6NHd5UVlaqpqbGkboAAGCQEACcQWAzuc985jMKBoO6++67nS4l7/j9fvn9fqfLAFBE7DBmsG1iR4kd2NihTik477zzJEmdJ6YPbIZ7gxrpG5PL5dLatWuzXVresMc5R4fiv78M9lgXps+bNy/nNQHZRmBTZCLr2MQENmOKTofGQqwAMoXjCZA5x48f109/+lOFQjObvxoAUkFgM7mOjg6nS8hLoVBIN998s2655RY+mwBkzGQdNmP+kIa7RyWVVofNunXrJEU7Z6ZiP2fFihWqqKjIal35pK6uTl6vd8L9g/3WZ5M90xBQTDxOF4DMinTYKLbDJhD3GAAAyC//5//8Hx04cEA+n0+33HKL0+UAKDKxIQ1r2GAment7dezYMUlSX1+fGhsbHa4IQDGww5ihjvjAxv65tra2pI435557riSp9+yoQmNhuT2TX1ffdXo47jWlwjAMNTU1qbW1Ne7+oT6rw6a5udmJslAgCvVCJTpsioy9gFvIiC7GZa9fYz8GAADyy4EDByRJ27dvd7iS/EInH5AZsSFNoZ64AgAKn30h8Uhf/HSLI73Wz6W27vLChQtVVVWlcMhUb+volM/tPmWFWqU0HZotWSgzNN5hQ2ATj/On4kBgU2TsKxFCigY2IQXjHgMAAABQOpgSDQCQD+z1Rvz98QvI24HN3Llzc16TkwzD0Jo1ayRFA5nJdJ+2Hl+9enXW68o3DQ0NE+4bGQxN+hhQ6DI+JVpbW5v27Nmj48ePq7+/X8FgcPoXjbvnnnsyXU7JsUOZsBH9vYeMUNxjAIDsCgaDOnbsmFavXs0VLgAAx8WGNKxHAqTn8ccf19///d/ry1/+si699FKnywEKSmNjo9xu94TPopE+ayr/UlxAfvXq1XrhhRfUc3byDhvTjHbglGJgU19fH/dz2DQ1MhRO+lip48Kc4pCxwGbXrl36zGc+o23bts36zUFgk766ujpJUljRD7/weLdNbW2tIzUBQKn54he/qB//+Mf69re/rRtuuMHpcgAAJY4p0YDM+fjHP67R0VF99rOf1W9+8xunywEKisvlUnNzs9ra2uLuHx2fIq0Up7daunSpJGmgwz/pc0b6xjQWCMvlcmnRokW5Ki1vJIYy/uGwNP51xh4HhYULRotDRqZE+/73v68rr7xSW7duVTgclmmaKf9DZlRWVkqSwkY0sDEN6wStqqrKkZoAoNT8+Mc/liR961vfcriS/MVnP5Ceb3/723rXu94lv3/yk3vAxpRoQOaMjlpXub/22msOVwIUpmRTWAUGg5M+VuyWLFkiSeqfIrDpa7eOOwsWLJDX681JXfkkMZQZHbLGPCsrK1VWVuZESXmL73nFIe0Om3379ukjH/lIpJ3RMAxt2LBBl1xyiZqamkryQOKk6upqSVJY0avo7G4bAhsAyC2mnZlcOByW2+12ugygYH3lK1+RJD388MO6/fbbHa4G+Y7ABsg8/paQKt4zlmShjH+gdNdenklgYz+2ePHinNSUbxJnDPKPWGOeNTU1TpQDZF3agc3XvvY1hUIhGYah888/Xz/5yU90/vnnZ6I2zILdYaMkgU1FRYUDFQEAYIltzw6FQgQ2QAYMDAw4XQIKAIOEQOYx7QxSxXvGkmzNEX8Jd9jYIcxwb1BjgXDS59jTpdnhTqmZENgMh5PeD44zxSLtKdG2bt0qyQoDHnnkEcIah0VCmZi/T6ZEAwDkA67wRjp4z2AmeJ8kx/EXAJAvppoSrRQXkG9oaIiM1w10Ju+y6e8ISCrdwCaxk2Z0JJT0fvA9r1ikHdicPXtWhmHojW98Y0kufJVvoh02UeZ4tw0dNgCAfMF0cUhV7KLpiOIqOswEgQ2QefwtxTNNU1/5yle0ZcsWp0tBnks27Zl/MDDpY8XOMIxpp0Wzg5xSnRIt8QL0wIiZ9H5wblAs0g5s7D8Owpr84Ha7Jyy4xZRomKn7779f999/v9NlACgBDL4jVbxnMBOcpCYXO7DM3xKQGRxv4u3bt0/f/va39ZnPfMbpUpDnEjtsxvwhhcanAivFKdGkaBAz0BlI+rgd2JRqh82EwMbPTEIobmkHNitWrJAkdXV1pV0MMqO6ujr+DsNMfj8Qo6+vT9/85jf1zW9+k/nwE+zatUt33HGHDhw44HQpQNHgqlSkikFmzAQDqMnFdjXyt4SZ4HMaqerp6XG6BBSIxC6awLA1HZrX6y3ZcSs7sEnWYRMaC2uwKxD3vFIzIbAZZZxzMnx+F4e0A5s/+ZM/kWma2r59O1/+88Rki26xGBemMjY2FrkdDAYdrCT/fOITn9CePXt0zz33OF0KAJQsvmdiJjhJTS7274e/JSAzON4As5PYRRMYCETuL9ULLyIdNkkCm8GugExT8vl8mjNnTq5LywuJwYx/fA0bAhsUq7QDmw996ENauHCh2tvb9cADD2SiJqSprq5uwn0VFRUTpkoDMDNtbW2SpBdffNHhSoDiwYAhUsW6R5iJUh3omU7shTmxtwHMHscbYHYSO2z8g8Gk95eS6JRoEwMbe5q0xYsXl+xxJzGYCYyGk94PFIu0A5uGhgY99NBDqqqq0l133aXNmzdnoi6koaamZsJ9dNdgOlwhBiCXOOYgVYR8wOzFBp6En0BmlOrA6WT4fUyP77+WxA4b/0Aw6f2lxF4XfKBr4ho2g11WiLNw4cKc1pRPEqdE848Q2EyG40xx8KS7gSeffFKSdO+99+pTn/qUPvShD+mBBx7QbbfdpvXr16uurm7GH9zXXHNNuuVAUn19/YT7knXdAEA6+CKAdPD+wUzEvk8YZMZMcGxJjg4bpIOB+OQ43sTj9zE9/pYsEzpshuiwsQOb0YExBf3x33ntEMd+Tilyu92qrKzU8PCwJAKbqXCcKQ5pBzbXXXdd3JvBNE29/PLLevnll1PajmEYnDxkSLJuGgIbTIcv2AByiW4JpIr3DFJlmiYnrePosEE6OE9IjuMLMDs+ny9u8D0wQGBTW1ur6upqDQ4Oaqg7fk3hwfHAppQ7bCQrnLHfM6PD1nkBswlNxGdTcUh7SjTJ+gJn/0v8OZV/yIxk4QwHMaSCAzyAbIj9rOdzHzNBhw3SEQwGp39SiYi9MI6/JaSKcwMAmRY7/Zl/KDDhvlK0YMECSdJQT/y0aIPd1s/z58/PeU35JHas0z8UmnAfLJxnF4e0O2yuueYavsDlmWQHLA5iAACnxXZI0C2RHN+p4sUOLNOJjVSNjo6qrKzM6TLyAn9LQOYxKIZU8T0vqqGhQWfOnJEk+QfGIveVsrlz5+rw4cMa6o2/4MQOcObNm+dEWXkjdr3u0fEp0RjrnIjjTHFIO7DZtm1bBspAJhHYIF2cfGAm+CKAVBHYIFWxg8y8ZzATsWHEyMgIXebj+FtCquiKRao4N0AqYsOZIGvYSIp20Az1xnfY2AFOqQc2seOao+MdNnzPQ7HKyJRoyC8ENpiN2C/YfNkGkA2xAz4MGGImWCgdqfL7/ZHbo6OjDlaSX+iwATKPIAup4j0TFdstERi2PpdKfdxq7ty5kqSR3ujndNAfUmA4FPd4qYp9z4yNNyER2EzEcaY4ENgUoWQHLA5iADKNLwJIFWsoIFWxwR7vGczEyMhI5DaBTVTs3w9/SwCygXOD6XFhZFTsGFVg2Bp9L/XAZs6cOZKk4f7olGij/dZndnl5eVxgUYqS/f8v9d8JiheBTREisEG6+LKdHL8XID0MviNVhHxIVWxIExvelLrYv6VgMDjFMwEAyL7YMargeIdNqY9bNTc3S4oPbOzbc+bMKfnALzGcqaqqktvtdqgaILsIbIoQqTPSVepfBIBMIeSLx+A7UkVXwPT4zI4XG9jETo9W6ljDBungOANkBucGUdXV1ZHboYD1uVTqgY3dYTMaG9j0WbebmpocqSmfVFZWxv1cVVXlUCX5h3Xnio8nWxs+c+aMOjs7NTAwMOOTgmuuuSZb5ZQUAhvMBgd4pIr3yfQY4IgX+32AAUPMBCEfUhEMBuPeJ0yJFsV6UEgV32Gmx+8IqeI9ExUb2NhKfQDeDmVGBqKf06MDVmBjd9+UssTAJvHnUhZ7bs1xpjhkNLB5+umn9cADD+ixxx5TV1dXSq81DIOThwxJ9sGX7D5gMhzgAWQDgQ1SxULp0yM8j0oMaOiwieJvCengOAMg0yoqKuJ+9nq9Kisrc6ia/NDQ0CBJCo5Ez5NGB8fiHitlie+ZxJ9LGRe2FZ+MTIkWDof1f/7P/9HVV1+tLVu2qLOzU6ZppvzPSX/9138twzAi/7Zt2+ZoPenweDzyer1x95E8J+f0+w4ASgnTWyFVdNggFUNDQ1P+XMrosEGqYs+TuJgrOc4lkSreM1F0S0xUV1cnlyt+mHZ0wPr+29jY6ERJeaW8vDzuZwKbKDpsik9GOmw+9alP6Vvf+lbk53Xr1qmvr08tLS0yDEPXXHONBgYGdPLkSXV2dkqy3kBVVVW67LLLMlFCWvbu3auvfvWrTpeRUZWVlerr64v7GRONjY1NCLdKVexBnS+SALKBDhukKhAIJL0NJNPf3x/388DAgEOV5J9gMDofPoENkBkMiiFVvGeiEseoGHyXXC6X6uvr1d3dHblvdND6/KbDZmJgk/hzKePCtuKTdofN/v379fWvf12GYWju3Ll69tlntW/fPv3xH/9x5Dlbt27Vnj171N7ern379umjH/2oXC6XhoaGtHbtWj366KPaunVruqXMSjgc1oc+9CGNjY1p7ty5jtSQDbQKzgwnrMnxRRJANtBhMz0C83h0BSAV9oVhttiLl0pdbGBD+IlU8dkEINMSx6i4yNhir2Njs9ezSby/FCVOmefz+RyqJP/EXgzJZ3ZxSDuw+d73vhd5M2zevFkbNmyY8vnr1q3TAw88oG3btqmmpkbf+9739Jd/+ZfpljFr3/jGN7R7926de+65uvPOOx2rI9P48JsZBn+A2SPYmx5fluIR2EyPv6t4BDZIxeDgYNzPdNhEEdgAmRH73Y7veUgV75kopkRLLjGYGR2wPr+ZEm1iQFPqax7F4jxpZgrp95R2YLN9+3ZJ0qJFi3TTTTfN+HVXXnmlvvvd78o0TX3nO9/RU089lW4pKTt58qQ++9nPSpL+9V//taj+2GM/7MrKyuR2ux2sJn8kfkGKPXlFFF8kgcxg8D0ei14jVQQ2SEViR03iFGmlLDak4fsvUsW5QdTIyIjTJaCAcW4QxawwydFhMzk6bCYXe57E1OOTK6SLltIObE6cOCHDMCZ01sR+EE12UnD77bdrxYoVkqQHH3ww3VJS9tGPflSDg4N673vfq2uvvTbn+8+mqqqqyG2uVIhKHOzhhDU5vkgCyAY6bJAqApvpMZAalRjQMCValN/vj9wupJNV5AeOM8nxe0GqeM9EJY5TxY5hlbLEtWpGxwMbOmwmBjbFdNF9ujhnmplCuugi7cCmt7dXkias/xKbdCZOTxBr06ZNMk1TO3bsSLeUlGzZskW//vWv1djYqC9/+cs53XcuENgklxjQENggFQRZQHq48gepIuSbHp9NUUNDQ3E/T3UOUmpiQxoCG8wE8+EDmcffUhSBTXKJwYw5fihODHJKkdfrjfuZwCYqdmyTwCZe7EVLsbfzXdqBTXl5uaSJJ9F1dXWR26dOnZr09fYfWEtLS7qlzFhvb29k3Zz77rtPzc3NOdt3rtj/XSRaS2MR2MwMXySBzGAgNV7s4A9fJDETBDZIReJVc8PDww5Vkn8IbJCq2PMBLrJIju95SJXLlfYQXNFInM6KwMaSLJipqKhgXE902EyFwGZysecHJdVhs3DhQknRThvbOeecE7m9e/fuSV9/6NAhSbl9Q919991qbW3VlVdeqTvvvHPW2/H7/erv74/7ly9iD+Yc2KOYEg1ALhF+xosdcGfwBzNBYINUJAY0hXRSlm0ENkgVHTbJxZ5P8nsBMofAxpIssIm9IL6UJf4eCGyiYsc2+Z4XZZpm3PlBIZ0bpB3YnH/++TJNU4cPH467//LLL4/c/n//7/8lfe3u3bu1c+dOGYahpUuXplvKjGzfvl3f//735fF49K//+q9pXRVz7733qq6uLvJvyZIlGaw0PbEdNrG3S11iQEPyHMUJB4Bsix38YfA9Oa7WjUdgMz3eM1Gjo6NxPxfSSVm2cSKPdHCeEDUwMBC5bZomvxukhPfL5AhsLMnCGaZDsyR2ZRHYRLFWYXLDw8Nxn9uJ0yfns7QDm6uvvlqS9Oqrr8bNE7169WpdcsklMk1TO3fu1Ic+9CF1dnZGHt++fbvuuOOOyAfWjTfemG4p0woEAvrQhz4k0zT1iU98QhdccEFa2/v0pz+tvr6+yL+ppn7LNQKb5OiwmRkGf5LjCzaQHjpskCpCvunx2RTFGjaTiz15DwaDvG8wrdjjL5/ZUYkzixTS4E8ucYxJjvPsyTEzjKW+vn7CfXTYWJgSbXKx3/MKaZ2WbEv8XRTSZ3bagc3/+l//S5I1EP673/0u7rEvfOELkdubN2/WggULtGjRIjU2Nuq6667T8ePHJVmLjX3yk59Mt5Rp/eM//qMOHDigpUuX6nOf+1za2/P5fKqtrY37ly8IbJJL/GMleQaQTZysxqNbYnq8Z+IR8k2PwZ+oxCnRCumkLNtiL1IyTZOLljAtpkRLrq+vL+7nxAAHFj6zkSoCG0uycIbAxlJWVhb3vZf3TFTsWCeBTVTiuUEhrW+ZdmBz7rnn6u1vf7s2btyo559/Pu6xm266Sffcc0+kVTgUCqm1tVW9vb2R+yoqKvQf//EfWrZsWbqlTOnAgQO69957JUkPPPBA0bdbEtgkl3jg4kCWHCdlQGYwkBqPq3WRqtjAhmlMMZ3EjppAIEAwMY6LlpAqOhyT6+7unvJnWDifTI7fy+QYfLckm/6MKdEshmGwXvckCGySK+SLuTyZ2MiWLVsmfezzn/+8rrzySv3zP/+znnzyycjJQV1dnd7ylrfob//2b7Vu3bpMlDGlr33tawoEAlq5cqWGh4f1n//5nxOe88orr0RuP/7442ptbZUk3XLLLQUX8MTO7UhgE5V44Eqc6xwAkD2xA+4MvifHiXw8fh/T43cUlewEdXR0VF6v14Fq8kuyi5aqq6sdqgaFgA7H5BIDmq6uLocqyT+xn0ehUEgeT0aGm1AiEtcnKVXV1dVyu91xx2ACmyifzxcZhOc9E0Vgk1zJBzbTueGGG3TDDTcoHA6rs7NTLpdLTU1NOb3y2H7DHj16VO94xzumff4Xv/jFyO1jx44VXGBTWVkZuU3qHJW4+CyBTVTsF2wGf5KjWwJID90S0+M4E4/PpunxnolKNs3ByMiIampqHKgmv9BljlQxjWlyPT09cT/HrtOLKEI+pIrBd4thGKqrq4sLh5Ota1OqKioqIsdhxjqjYr/XJY57lrKBgYG4n/v7+x2qJHVpT4mW0s5cLs2dO1fNzc2cXGZZbFcNB7GoQp6/MNsSr4gCUsFJGWaCDhukisAGqYg7QXVb16XxXc+SePJOYIPpENgkl9hRQ4dNcpwbJMd3mcmxgHxUYkBDh00UU6IlF/s9j+94UYmBTeLP+SyngY2THnzwwci6OZP9+9znPhd5/tatWyP3L1++3LnCZ4kOm+QST1ZJnqM4KZseQfPkOPnATMSuJUFgkxx/S/Fifx8M/iTHeyYqbg2bMu/E+0pUKBSacMylyxzTiX3PsBZUFFOizQznk8lxPjk5pi+NSgxsGhsbnSkkD8V2YtGVFRU7tsnFSlGJAU1fX59DlaQuq4HNyMiIWlpadPLkyWzuBknEdtiwhk0UHTaTY55qpIP3DGaCwGZ6nMhH2RfO2DjOJMd7xhIOh+PCGcNrXalbSFMfZEuycIbABtOx155NvF3qEgd7ent7nSkkD8V+HhHyIVWseRSVGNgwJVpU7AXpjHVGxX6vo8MmKvE8oJDOCzJ6RDRNUz/96U/1H//xH3rqqaci8woahjFhYKazs1NbtmyRJK1evVo33HBDJkspebHtpLSWRiVeZVlIC05lW2xgwxfs5LiKeXKhUIirojAtBn+QisSrcwlsMJXE73hGWZlMFdaVdNlCYIPZYAHj5BLPH7kAMCr2HJLzyeQ4n4yqqKjQH//xH+vnP/+5JMntdjtcUf4gsJkcyz8kF9thw3e8qEKeEi1jgc3Bgwf1p3/6p3r55ZclTf9B1NTUpPvvv19HjhzR/PnzderUKblcJTNDW9bRJphcYprKF+yo2C/VtLAnx1XMk+M9kxwnZfEY/Jke75moxOMKx5nkeM9YEhcCN8anROPqdwIbzA4XWSSXOKU20y5G8Z6ZHueTUYZhxA2+E9hE1dTUxP1cW1vrUCX5J/Y9w1hnVOz3OpZ+iEocAy6kC7kykpDs379fmzZt0ssvvxyZvqKqqkpVVVWTvsYwDH3kIx+RaZpqbW3Vtm3bMlEKxsV21XDVe1TiF2q+YEfFDoQxVVFyDIrFi/198J5JjpOyeLEn7wwWJsd7Jiqxo4YOm+R4z1gmBDNl1kl8YpBTipKduHMyj+nEXljB4HtU4t8Ov5soAhukKvaibQKbqMSx1KnGVksNyz8kR4dNcokdNYU0JVragU0oFNKtt96q3t5emaapN77xjdq5c6cGBgb0vve9b8rX3n777ZHbjz76aLqlpO3zn/98JHC67rrrnC4nLbHzfxLYRCUGNIXUDpdtsQPuDL4nR2AzOa58x0zwRRKpoMNmZvhssiQGNoaPwMZGYIPZoCs2ucSpvggmonjPIFWxIQ2BTVRsQOPxeOgkiRH7PmH5h6jY2YOGh4c5PxhXyGvYpB3Y/Md//IcOHjwowzB066236tFHH9XrXve6Gb120aJFWrFihSRpz5496ZaCGLGBDYu3RRXy/IXZFnuywZzDyXEVc7zYq90J+TATsQOETEmJ6dBhg1R0dHTE/ewav+qyq6vLiXLySrLjLcdgTCd2wJ2LLKISAxoCmygWvU4uduCU88l4sR02LJEQVVlZmfQ24nFxelTi+mocgy2JU6ANDw8XzHhn2kdEe4Gw8vJyffvb3075IHvBBRfINE0dPnw43VIQg8AmucQOm0JKV7Mt9qDFiQdmIvY9Q2CTHCdlUYFAIO49w9XdmE7icaVQvlznGscZS1tbW9zPxvhCtK2trU6Uk1fosMFs0BU7USgUmjD4093d7VA1+YeQLzk6hCdHh01ysWvYMO3X5AhsohKPuXzPsyS7SL9QxoHTDmyee+45GYahq666SnPmzEn59c3NzZK4+i3TYkMaPviiEr9gF9KCU9nG4Pv0GBSLF/s+YSAV00n8EsnV3ZhO4nGFz6bkmPLAMllgk3h/KUq86lLiGIzpsYDxRL29vRO6Pbu6uugAHRf7PuE9ExV7MSTvlXgENslVjH+HkQhspsLF6VEENsnFjvnaf1YT1r3MU2kHNvb0A8uXL5/V6+1ElJPwzIrtdOIgFpX4h1kof6i5EDswxuA7ZoL3zPQYSI1K/NLIlZfJ8Z6JSjyucJzBVNrb2+N+dpVbZ2UdHR0l/3eVLJxJFuIAsZjeaqLOzs4J942NjXFOOS72PcP3vCj+fibHzDDJxYY0BDaTI+SLSjzXJrCxxAY2VdUT78tnaQc29uJXsz2JtgOfhoaGdEtBjNgDF3OBRiX+YQ4NDTH917jY3wO/k+RKfcAnEdPoTY+urCgCm5nhPRNFYDMzvGcsiYGN3WEzPDw8YUrcUpMsnCn13wmmx+D7RBP+bjzWeTYBqCU2mCCkiIr9XXChcjw6bJIrKytLehvx+A4clfg5xOeSdeyNPf5WVVqf2SUT2MybN0+SZr0Gza5du2QYhpYsWZJuKYgRe3UCgY1lbGws6clpocxfmG10S0yPLwTxCPmmR8gXxZRoSBULO88MxxnLhCnRvB5pvJM/McwpNcnm7yawwXRij7kENpbEATCjzDrn5juNhTVskqNbbXJM5Z9c7NosBDbxGJNJLvHzie95E2dUqq42kt6fr9Ieyd+0aZNM09Tu3btTniP697//vc6cOSNJuuaaa9ItBTFiQxoCG0tiimrI+nLQ09PjRDl5h26J6TEoFo/AZnp8oYxKHMzgRD45jjNRBDaTi13AmOOMJdnC38b4NCKlvih4spN2TuQxndjBH6ZWsSQeS1zlZUnvL1Wse5QcnUeTYyr/5GJDmtjwBphM4ve6ZBfrlBp7Ri9bba11zpRsetN8lPZI/tve9jZJ1mDvpz/96Rm/bmBgQB//+McjP992223ploIYhDQTJQY27vHAplDa4bKNwffpMZAaL/Z9wskHppN44s7VqMkx+B5FYDM5OmHj+f3+pL8TY3yQo9TDiWQn7ZzIYzqxgQ1Tq1hee+21uJ89TdaE+LOdbaTYxH6348KcKKYXnBxToiVHhw1SEQgEJozHlPp3X2liMFNbZ51nJwY5+Sojgc1FF10k0zT1gx/8QJ/85CenPaF++eWXdc011+jgwYMyDEPXX3+9Xve616VbCmIQ2EyU2PbmkvUhSIeNJXagg7l1k2MgNV7slwIGUpMj5IuyAxvD7Y37GZhM4okHgxxRscdcjjOTn5Qa3rIpHy8VyS5OYkpgTCf274bAxnLo0KG4n73NtZIIbGyx3+24MCcq9nfB7yVe7Pk1gU0Ua9ggFckuwuF73sQpkevrXUnvz1cZ6Tn8wQ9+oGuuuUYDAwP6+te/roceeki33nqrXnzxxchzvv71r6u1tVU7duzQ008/HTm5bG5u1ubNmzNRBmLwYTdRYmDjNrySSYeNjSnRkCq6sqZHyBdlD767PGUKhYK8ZzCtxEENQr6o2M9sAhupq6vLuuH1SjG/G8Pnk1Q4Ux9kS7LvuoUyfzecYZpm3HuEjixLYjDjnWMFNolBTqmK/dzmMzsq9vcSDFrfgRmEtxDYJBfbYcOUaJgOgU1yiZ009eMdNiUV2Fx44YX65S9/qdtvv13t7e1qbW3Vt771LUnRA/AnP/nJyPPtE8sFCxbo4Ycf1pIlSzJRBmLwYTfRZFOiccJqiR08ZaoVzAQLaCIV9nHF7rDhOIPpJHbU0GETFXvM5W9JOnnypCTJqK6WGdM57aq2pis6ffq0I3Xli2SBTV9fn0zT5MICJDUyMqKXXnop8jMDP1aX0f/P3ntHyXHdd77feyt1jpNnkAaBBHNUtGRbx5a0lrxr2RJXWnst2fTZYz3vrt/6nLfeJx/Llp/Wli3J1tqWbMmiqECKJJgzCJAgMkDkMMAgDsIAMxhgcuyZDvX+uH2rw/TMdE9X6q76nINDoru67q8bVbfu/X1/obe3t+C1fMHGvZ8KM7HcTJIcxRlq09PTrmCTJT/oxPVh5XBLorlUQqmqQW5gOjAwMFDwd55h45iSaJyPfvSjOH78OH7v934PkiRBVdUF/4iiiC996Us4dOgQHnroIb1McMnD6YvFUhRPYgJhD0FXsGG42RIuleLWY14aN/I9B3cq06xgk06nCxqnuzDcayaH5uyhQuHfXdys2CK4E5X6/QWvk6xgwwUdp1JqrZtOp92sCZeycQWbXHYN8Svaa2I8CFCCiYkJ9Pf3W2WaLVBVtaCMntNLUeZTvH5x1zOlcQWbHPkijZthU4i7V5qPm0ldmhs3bhT8PRJlfvL+/v6auI50ybDhNDc347HHHsPf/u3f4u2338a+ffvQ19eHsbEx+P1+NDc34/3vfz8+8YlPoK2tTc+hXYrIf9jVwoVoBsUbDZq9/N0NCMNtIL807r1UiFuPeWlc8TyHNq/Q3PNpbm4OXq/XIovsiXvN5ODzCvV4kZmedOeZPNys2EI0wSYQRL4MTF3BBkCJyEtBANJpjIyMIBQKWWOUS03hinvA/v37AQByawSzF1jULhEFSM0RJPtHsH//fvzmb/6mlSZayszMTEEGktv3KEexeOWKWaVx+zDnyBds3L2By1KUyrAZHh62wBJ7UZwV2xCnIIQFGw8ODqKxsdEiy8pDV8GG09DQgM9//vP4/Oc/b8TpXcogf1J3J3hGsepMIZV83ank1xl2syVK495LhbiCjUslcGfP3Fgu0iWRSLiCTRGuMJxDE2wUnyvYFOFm2BTCN2SkKMOGCzbXr193bLmimZmZ+es6jwJMTWNkZASrVq2yxjAXW1M8r0xNTSGVSkEUDXEf1AQ7duwAAHg6WzTBBgC861qQ7B/Bjh07HC3YFOOKEjmKxStXzCqNm2GTIz+rxt0buCyF1ssxD6cLNqqqziuJLIgE8TjB4KCKq1ev2l6wcSXsOsWJG9KlmNfDhrg9bPLJF2zcJpEu5ZAfbelGXpbGXWDnKHT+sGeUKw67LAZ39lAPE/VcB0cON8OmkFyGTaDgdeJjAg6PpHMi2oad5gVzeVhJp1IRmS4uQGlnu5OD3CYnJ3H06FEAgGddc8F7nvWtAIDdu3cjk8mYbptdKK7Q4FaxyOFm2JSHm2GTI18cd317LktRSpwpJeI4iZs3b2JmZgbFt09TE5tnaiH73p0R6xR3Up9P8YRFsz1snK48c/JFGjeKuTSu870QV7BZGncuzlEgBGfLorni8HzcayZHTrDxAWDChFuyk+H2ncuhqiquX78OAKC+oh42ggDiY9dPcVkEp6CtcxWP9hpR5ML3XFyKKBDzZDfI7ejRo0ilUhAifoiRQmFYbo+BSCLGxsa0PjdOpFjQc/L1UkzxPskVs1yWwm1x4FIJpYKShoeHHR1EcPnyZQBAY2Ph3rqlVSh43864go2LY7h582bB30UoJV93KvmRPm4Us0s55G823I1HadwFdo78eYUKTDB3xWGGkxfTi8HnFZLnaHbnGka+cOV0EWtoaEgTf4tLogGsrw3gXMGGb+KJN0+wyZaidGrW0WK4z21GvrOdeJV5rzmNw4cPAwCUlQ3z3iMChbwiDgA4dOiQqXbZieLrY3R01L2fshSvXdxAN5elcAO4XCrh1q1b815LJpOOzozlggzPqOG0ttCC9+1MWUVof/rTnxptBwDgd3/3d00Zx8WZFG9KBZKLLnR6TWagULBx07RdysHNsFkad7GdI1+wIYIEJGfc6yZLfkkr17mRg28yZs4cBQQRSKcwMTFh+3rDZpBfTtDpmWq8pAHx+UGE+bFoJBAAbg7Mq2PtFPgmnngV8NmFizelNvhOZ25uDoqiWG2G5eQ7eYhHgTo25eiMrGPHjgEoLdgAgLIijtmeARw7dgy//du/baJl9qG4xGI6ncb4+DjC4bBFFtmHYjHLXf+6VIK7N3BZimJfp9cHzEyzdV40GrXIKmu5cuUKAC7YpLXXm7OCDX/fzpTlof7Sl75kuNOJEOIKNi6GMTU1NW8RKUAGAYGqqrhx4wY6Ojosss4e5C8c3Qhml6VQVbXgOnFy9MZiuAvsHPlCMBGlea85mfwMCVfky5H/XCKSDDWdcp9PWfJFGqf3glqofw2Hv14LtaqNIJdh49VeIx5PwXtOpvg5PTMz4wo2KHS+U6+EDJydYcMdO2JDqOT7UiN73amZfEDhNUMUAepsGsPDw65gg/l7a3fflMNd9y6N+xu5LEVx+4dgiGJmOuPoPjZ83V+cYdPcnBNsVFW19f1Vdkk0VVUr+rPQZxY7l4uLUWjRlyhs3iYStnmtBXXVSFRVLVhkuw6x0th5MjebiYkJpNO5SAW3cbHLUuRvTqkoz3vNyeQ73FOplIWW2Itiwab4NSeTX07Q6aUF+RqPBhcSbIIFxzkNXvqXizQAQHzegvecTHEPKKdnrHEKSqJ5nF0SLZ1Oo7+/HwAgRueXXQQAIcJed7Jgk5+BJfrZNeNkZ2E+xXtrd6+dw/UDLo3rgyjE/T0KyWTmCzOBMHP1O3kOLsywycH/PjExYXsfVlkZNl/84heXPCaZTOK5555DMpmEqqqQJAl33XUXVq5cCb/fj6mpKfT29qKrqwuzs7MghECWZXz2s591fCkqF+PhN6sML2aRc/bIxIekOo0rV67gwx/+sFXmWc7MzAxu3Lih/d2pG7KlcBeUOYof/k4uk7EY7oIyR744I0jOdv4U4/YjKU2BYJPNynIFG4abYZNDC8rJCjPF0KCzBRu+viN+n/YaF2zy135OpVigcQUbRn72FfUxsc+pAt/Vq1eRSqVARAFC0AM1Nb/vnBhjgvHAwAAmJiYQDJaej+qZ/OtDCnqQHJ7GwMCAhRbZh+L1rrtvyuHulZbG9UG4LMbo6GhBIC0ABIJMlHBq6dtMJoNLly4BAFpaCwUbRSFoaCAYHFRx6dIlxGIxK0wsi7KUkscff3zR9wcGBvCZz3wGc3NzaG1txde+9jV8/vOfR6BEaYKpqSk8/fTT+Iu/+Av09/fj4sWLePHFF9Hc3Ly8b+DiUgb8ZpWIF7NqztkjEV/B+06l2NkzOTmJ2dlZtyREEe6CMgcXbATRg3QqgZmZGUxNTcFfouGziwtQuFmlAptb3A0rIz/Cuzja28kUlNGTmGDjRqUy8p3KTs+w4U1D6RKCzcDAAKanp+Hz+UoeV6+UFGyy/3/jxg3bl4MwmuL7x+n3E4dnlAAADbHrpa+vzypzLIX3r5FaoyCUQsV8wUbwKRAifqRHp3D8+HH8wi/8gslWWk++ACyFmMiXfx05ldnZ2Xnzirv+dXFx0QsujPuDwFTW1RmKMJHCqYEW/f39SCQSEASgsXF+YbHWNgGDgylcvHgRDz74oAUWlkfZJdEWIp1O45FHHsF7772H++67D8ePH8cf/MEflBRrAMDv9+PRRx/F8ePHce+99+K9997D5z73uXmKoIuLnvDNPBdoOPzv/H2nUipV0snpky5LwyMvRdEP/ihxr5n5uBFRjEwmU1gPX3IFm3ySyaT2/65gwyh2cFDRzcrKJ/+3cXJGQCqVwpkzZwAANBopeQxRFK1/y+nTp80yzTZogo0vryRa9veYm5tz/Dw8NTW16N+dSn5mBA2y/ZJTne9Hjx4FACgr4osex9/nxzuN/GtGirhZfJxS+yOnz7suLi76wefeUETQXgtFacF7TuPChQsAgJYWCkGYH5TU1kYLjrMrVQs2Tz31FHbt2gVBELBp0yY0NDSU9bl4PI5nnnkGlFLs2bMHTz75ZLWmuCyA6zAEenp6AAASKYz+l7OCDX/fqeQvGgXCnGJuI9r5uPdSDr4Bm00MQRCZE8gVbFwWYnx8vCAwg0ruNZNPvsPdLYnGKK4pTGVX5MvHzbBhXLx4kf0WogQaLN0MHACEOHOknjhxwizTbEEikdDWczQvmI4IglYWzalOeI4r2JSmQLDxe+a95iQOHToEAJA7Fhds+Pv8eKeRH8kthtj84tRrJh83MNLFxcVIuF8mHMm598NRJt44dY13/vx5AMCKFULJ9zs6hILj7ErVgs1Pf/pTAMAv/MIvYN26dRV9dv369fjIRz4CVVXxxBNPVGuKywI4ucwBwJzsXJCRibfgPTkr4Fy7ds3RTrL8RaNA3SaR+eQ3AHf6vZRPwTWTFWxckY/hZozOp/jaEGR2zbjOd0Z+WUonZ0vkU3xtEMUVbPJxM2wYx48fBwAI8RgIXXhbQ2PMkXry5ElT7LILWgkrSQRkueA9EsitgZ1MsUCTX4rRqaRSqcIeNgG2f7p165bj1jgjIyM4d+4cAEBZtXhgqrKqEQDLsMnPnHUCqqrO62EDuIINUHp/NDIy4rhrxGX5uEGjLovBBZtIPCdORLIZNk7NcuTP7Y6O0nuDFStowXF2pWrBpru7G4SQisUaDv9cd3d3taa4uJSkr68v2+yazMuwESCDQkI6nbb9zWok+Q4wkbiCTT5OFvIWoyCKTnQ3Zfnkl7RyF9iM4s0qdUW+Alzn+3yKhRk3K6uQfCdzIpFwrOOHlx4S4os7UoWGnCPVSXAxhgQC84JOXMGGwfYIOdw+WfObFBOfByAEmUzGcc9tni0jNoYg+D2LHis1hUE9Mqanp3Hq1CkzzLMNY2NjBXsmtyRajoXuGTcAheHulZaGLhKQ4uLCs2jCsdx1Eokx8ebGjRvIZOb3Xat3eOYMz6Qphr8+MDBg63Vf1Xc+X9BNTEwscWRpeBST0xZ/ZuL0hyBfMCskAEoKL3lCCDwkWHCcEynMsGERmMWbNaeSH/nuxIfdQuRvwETJP+81J5O/YXWzshjFTnYq5wQbpz+jALe8VSnmXTMKc/64Dg5G8brbqVkBR44cAQAIjY2LHic0xAFCcO3aNUcFF3Axhgb8896jQVYirbe311Sb7EbxRt3OG3ezKF7PEUpBsmKF09Z6XV1dAABliXJoAEAogdwRK/icUyguuyOHc8FcTsvKKqbYzyUF5ZKvu7i4uCwHLcMmmvN1hqIUhLA+qU7bO2UyGa03TceK0pKHz0cQizE/jZ3LolUt2DQ0NEBVVezcubNip0smk8GOHTsAALFYrFpTXBbA6Q5DbaGdFWaKUWio4Dgnku+8EAU3WyKffEeqqqpuQ/AsBdeM5NbBzyf/GnH6JpUzL8Mme83Mzs461tGcT75Iky8SO5liwYaXRHMzbBiukxkYHR3FxYsXAQC0YXHBhkgyaCQCICfyOIErV64AAEh4/hqYhNhrV69eNdUmu1GcYTM6OmqNITailChDQ74F36tnTp8+DQCQWiNlHS+1RgE4r3pI8R5ADHoASpBOpwuy8p1I8bpFDrjrGZfKcIPbXBYjJ9jkskkEgSAYdmZZtGvXrmFmZgaSBDQ3Lyx58CwbO1daqlqweeihhwCwi+Dv/u7vKvrst771LfT394MQgocffrhaU1xcSsIbzHJhphgu5DitEW0+BeWtqCvY5FNcnsiNfmfkO+BFwTvvNSeTf824zndGccYeFQQQQSr5nhPJn1fchteMhbKyhoaG3I0r5mfULDfTvZY5fPgwAIAEQ6CexUsVAYDQ2FTwOSfABRsami/Y8Nf4MU6lWLBx4r1UjNb7KA8h6FvwvXolnU5rfa/k5khZn5Fb2HG8v5ZTuH79euELhEAKuwFdwPz9kexm2LhUiNMDsF0WRlVVbY4NRQvd+7xEmtMEGx6I395BIQgL3zsrV7Lfhwdm2JGqBZsvfelL2v//2Z/9Gb7+9a8XNOkuRTqdxl//9V/jK1/5SsnzuLjohaqqmhDjIaUFGw8NAwDOnj3rWOdq/iQuUGeWPFgIt+zMfFKpVEEEKu9H4rR024XIn0ecOqcUU2pTKsiu0MfJz46Ym5tzrxuUEGyyPWySyaQ7D2P+s8mJTuaDBw8CAMTmprKO54LNgQMHDLPJbly+fBlALpsmH/7a9evXHdsDCZifUePEe6mYUiIejQQWfK9e2bdvHwYHB0E9MuT28qqBKKubAEpw5swZW0ft6k0pIU/O9rGZJ+Y4jOIgSF4uzt1ru7i4VMvExIQW+Mf71nCi2b87KdACgBZosXatuOhxa9ex9+0cYFG1YPMbv/Eb+Hf/7t9BVVWoqoq/+Iu/wNq1a/E//+f/xIsvvojDhw+ju7sbhw8fxosvvog//dM/xdq1a/Hnf/7nyGQyIITgk5/8JH7jN35Dh6/jUgonR6JeuXIF4+PjIKBQSKDkMSIUCJCRTqdtra4aSf6CUaJuNFQ+xWVmiiMxncjIyEjB3wXRjRTLx82WmE+pa4M74N3rZv684s4zJUqiiQKIIJZ8z4lwpzJR2H3kRBGLCzZCU3NZxwtNTLDp7u52RAk5VVW1/jQ0WEKw8XkBQUA6nXa0Q9Vd582nlCgjRJ2XkfXCCy8AALx3rQARSzcuLkbwK/BuaAMAvPjii4bZZjdKzSFy1Lfge06iWJhRIq5g41IZbh9dl4XgYow/SCDJhdkk0QZnZtjwgP21nYs/tzs7cyXR8nsQ24mqBRsA2LRpEz7ykY9owkBvby++/e1v47Of/Sze97734a677sL73vc+fPazn8W3vvUt9Pb2asd+6EMfwjPPPKOHGS4L4OQUyqNHjwJgZc8IKX25E0LgyZZLc1Jdc04ikSiILpTyylvZdeIyE7dPwHyKa1GLIrtmhoaG3J4tKBRsZmZmHC2ac0pm2Ehuhg3HFWzmUypjj4sTThdsZmdntdKLxM+CUZzWd2NqagqnTp0CkMucWQrq84EEg1BV1RHrvaGhIW0dRwK+ee8TQkACfgDOi77MpzgIpfjvToRnZuVDs4JNqffqkdHRUWzZsgUA4L9vdUWf9d3Ljn/xxRcd0/uyVC8sOe5f8D2nkMlk5jlLPVE3ONKlMpzszyuFu7fOwddv0fh8cYK/5iTRXFVVLQh/zRKCTUMDQSBAkEqlbJsRq4tg4/f78c477+BrX/savF72AOIZNwv98fl8+Mu//Ets27YNgUDpzAcXl2rhG3Je9mwhPCQCICfwOIli5zslEkh2anB7S7gl0UpRfF0IogcAQSaTcbwjFSjMqkmn067widJziaCw9YLTm9EC8wUaVxguLdjk97FxMvkO5cwwEzydJtgcO3YMqVQKxO8HrWAfITY5pyzatWvXAADE7wMRSm9aaTBQcKwTKb53nC7YJBKJ0j1sYkywuXbtmiPWNc899xwSiQSk5nDZ5dA43g2tEIJeDA4O4q233jLIQvugqmpJIU9pZPOLU0S+UgwPD88rOSmHFQDOi3p3cXHRH75+49k0+fDXnCTY9Pb2YmJiAqIItLcvLncQQrBmDVsf8yAwu6GLYAMAoijiz//8z9HX14fvfe97+NznPod169YhHA5DEASEw2GsW7cOn/vc5/C9730PfX19+OpXvwpJkvQywWUBnKxAc8HGmxVkFsJLI9rxTvu9iuvqEkIgZvvYuI5UV7ApRbHznRAKQWSbD/eamV8Gzell0ZLJZMnrQpBZ5KW7YXUzbEpRSpThGTZO75dVSpxxmpNZK4dWZnYNh5dP45+vZ/gGnWfRlIK/52TBpniuGRoacnT5mcuXL7O9kFK4Ryc+D4gsQVXVui+Llslk8POf/xwAEHjfuoqj24lA4X+wEwDws5/9THf77Mbw8HDJ/ZHS4Ao2fI0rhWTtNSXsLXjPxcXFZbnw9Vu8cX5gDn/NSWu8rq4uAEDHCgpRXPrZvXo1k0R43xu7sXgXnmUQCoXwh3/4h/jDP/xDvU/tskycmkI5MTGhpbYtlWGjkCAAglu3buHatWtYsWKFCRbag2LBBgBE6kEyM+0uJDE/0t1tRlvaMSgIHqRTCcc5DUtRvGmdmppCPB63yBrruXXrVlYIJwBygrigsBI97jwz/55yWrZEMYlEoqC0IIdn2DhdsCn1/Z32m/ByB0JjY0WfExoatc+rqlrXa2Rehohn0ZSCODzDJp1OzxNsUqkURkZGHPvc7unpAcBKoGVu5OYVQghoLIj0jWH09PRgw4YNVploOAcOHMDVq1dBFAm+u1ct6xyBBzsxvvM0jhw5gp6eHnR2dupspX24cOECAECKepEcmdFeVxpZVtbNmzcxMTGBYIleWvUOz1bzRLxIjrPyeJ4oC3IbGRnBzMyMVqHGxWUhnBZQ7FI+vFdhvKmEYJN9bXBwENPT0/D55pfHrTd4wP769eVJHevWiwDmbFsqWbcMGxcXu3H8+HGoqgoRXohEWfRYSoSsaOO8smil6ueKgltbl1PsSHUj3xcQbERlwfecRrGj2ekZNlyQoXLhhlSQXcGGUzyvOP0+yokPhc506vawAVD6+nCaYMOz9qhv4eyRUpDsZnV2drbuAzD4Jp6EFnaS0ux7Tu0xMTQ0VOgIU1gUvJNLAl+6dAlArgRaPkKc9fzkok698sorrwAAfHd2gMrLi28Vgl541rYUnK9e4dcDF2g4gleCGGLP7YsXL5pulx3gmY6eqEd7TfRKEDzsunJy/zCX8qnn4BKX6lgsw8bnp/D62LXjlLJoPIP+ttsW71/D4cedP3/elvtvV7BxqVu48OJdIruGw49zmmBTaqEoUVew4RRHuruCTWlnqSCwjYjbQN7NyiqGCzKCUuhY5Rk2AwMDjo4cU1XV7aFQBHfGE0+hyEezfY+c7EwFSs/BThOx+DVAKoxMJqIIZMsx13sJT02wKSPDhh/rNOaVePW7cwx3rAvREoJNrP4Fm9nZWWzevBkAlp1dw/HdvRIA8PLLL9f1OoeXPOMl0PJxelk07iRVYrnIdkIIvDE21zg1u9HFpRpcAYuhqmouw6aEYAMAMQeVRZuYmEB3dzcA4Lbbygu2CIUo2tqYLHLo0CHDbFsurmDjAFpbW602wRK48LJUOTSOh7Dj7JoOZxSlBBueYeMUJX4xih2pTotiLkXJMnqS6+TgFIt6Ti9vpQk2C2TYzMzMzBO5nMTY2Ni8fgmuYJPNnvAUpu7zv9e7o30pSgnjThJsksmk9hsQX+WlZKjXGdl9fBO/WEk0GmLvDQ0NOTIbtPheIl6l5OtOQsuwKSnYBAuOqUdee+01jI+PQwj5oKxqqOpc3tvbQWQRvb292L17t04W2g/uCJSi88vtyFmhwqmZJPy38cQKn1WeuK/gfReXxahnwddl+YyNjWml2KMNCwk2zOXvhMCcgwcPIpPJoLmFIh4vX+rYeAcTd9577z2jTFs2rmBTx/zkJz/BN7/5TaxevdpqU0wnk8nkBBtSpmBDIwCAM2fOOGrTWiqLxs2wyVHsOHUFm9JOLlHKZUs4HTdbopCcYFO4kSeCCJotpefkucbtRzIfPo/wnjUcnmHj9HmmlDN5cHDQMRv6/v5+pNNpQBDmZWGVAwnUf9+WVCqlzauLZtjIslYGrJ5/j4WYl2Hjc3bwiaqqBT1siqF5GTb1ON+oqoof//jHAIDA+9aC0OpcJVQW4b9/DQC2L69XeICfHJ0/H8sxll3tBGdhKbSSaAsINm5wpEs5uBklLqXgwRPhKIXiKX2NNDYzMcIJWY779+8HANxxR3nl0Dj8+JoVbARB0P6Iorjge9X8KT6vS/V8+MMfxmc+8xmrzbCE8+fPY2JiAgQCFLLwRjUfiXggQkE6ncaJEycMttA+lOxhkxVsnBoNlU+xs91JUcylUFW1tGAjso2Hkx3vHLfvUSFXrlwBAIie+XOxkH3Nqf0TAFewKQWfR3gmBId6mePnxo0b87KSnEQpZ3IikdCi7OodPl9Qf2BZTgyecVLP805O1KJLZiHRgHPLohVn6/HfyqlZfLdu3WJBa4RAiJR4ZmdFnImJibpcDx88eBDd3d0gkgD/A526nDPwvnUAAbZv316XpeRUVdWchkqsREm0OHtuO8FZWIpcD5vC9QwXcNy9touLy3Lhc29T68ICRVMbe88JfcR4Juudd1amLdyRzbDp7u62XcBOWYINj6BRVXVeNE3xe9X8cXHRi8OHDwNg2TWElB8dxbNs+OfrnYmJiZLR/5LAFtfDw8OOcQCVIpPJzHOcOt2ROjo6WjIDTZTZNePECN1iiu8pp5dE45t00Rua957kZRmQ9VxeZSlKLQzttlg0G+7gELyFfY+oxwcQgmQy6ejfaF6GUTboySmZR1xo4ZkylUIDwYLz1CP8WUwC/iVFLRJ07vO7OACF97BxavAJd+jQsB9EmO8AIqIAmhVyLly4YKptZvDYY48BAHz3rILgU3Q5pxQPwrOelSfn2Tv1xMDAANsXUAK5wT/vfaWZzbdOcBYWMzIyopX89cYKM4a9DUzAcaqQ5eLiUj18Xm1qXVigaG51hmDT19eHc+fOgRDg7rsrE2zCYYo1a9jvtGvXLiPMWzZle7IXE1RcscXFbuzZswcA4M0KMOXCj+efr3e4k5QSueB1gUoQCNuoOHkhOTIyMm9+Gx4edvScxx2pVCjceEgy28DfuHEDc3NzpttlJ9ySaDnS6bTmFBU888uriN76r4e/FAsJNu48A1BvoUOeUKr1sXFyGZF5WQFeZ5WkzPVmmT+nlIMTMkp4ZuNi/Ws4Tsg4WohiwYZmBZt672+0EOfPnwcACA0Ll5MW4iz4ot4Em0uXLmHbtm0AgOAHNuh67uCHbgMAPP/883WXmcSdgErcDyLMdy0pjWyeHh4edlzQG79HPHEvBKXQgehvZb9LT0+PozOGXcrDyXsCl4U5c+YMAKBt5cICResK9t6NGzfqOoh0586dAIB16wQEg5WXM733PvY7bd++XU+zqqasb5LJZLQ/6XR6wfeq+VN8XheX5ZJMJjXBxU/jFX2WH3/kyBFMTEzobpvd4E5SWZi/oZezWTb1mL5fLqX6BCSTybp+2C0FdwJxgYYjCB4QKkJVVUc7UoH5go2Tr5f+/n4kk0mAUAjK/LI8POuGX1dOpJSTPZFIOOIZtBCayOedH61LfWzuceo1k06n5zm9qMIEdKc0StdKoi0zw4bkZdjUqxOEr+9IeOk+jiSc60viNOb1sMkKNk65l4rhDmYuypSCizlc3KkXHn/8caiqCs+GVkiNC3//5aCsaoTUGsXs7CyeeuopXc9tNefOnQMAeJpL/2aCIkLOlgOrt2tmKfj95G+ZH1zgbfCBCATT09NuWTSXJXF72LiUoru7GwDQvmphwcbro4g10oLj6xEutNx33/JarfDP7d69m/kubEJ1nfRcXGzI0aNHMTk5CQESFFLZglsiPkjEh3Q67Ygsm5xgM98pJovMEeLkDJt5kZfZTCSnRDGXgjt0ZLlw80EI0V5zotOHMzMzMy/DyMmCTa5/TbBkeUqeYePEyG7OvF4JggTAufPM0NCQJkgI/vnPcDHAnIX1Ft1dLiMjI/OicUlWsKm3yO2F0DJsAtVl2ExMTNTt/Kw1jg8vvQ6mEeeWppyfrebRXq9XMW8xuPNdaIgseEw9Cjajo6N48cUXAQDBD96m+/kJIQh+kGXtPPHEE5idndV9DKvg14FSQpTgeFpCBcc6BZ595G+ZH1xABQpfkxsc6eLisjwGBwdx8+ZNEAK0rVi4hw2QE3ROnz5thmmmMzs7i7179wIA7r9fWtY5WGYOwfj4OI4cOaKneVXhCjYudceWLVsAAD4aX1Y0gp82FJynntGyJUoINvw1Jws2xQ5TUXR2qQwg59CRlPmbD0kJFRzjRNwG8oVwIYYLM8XwMmkDAwOYmZkxzS47UTzPCIqzylsVwx061BcAEedHSQnBSMFxTkPLCJBypUyJ4qysAB6NTALz1y7lQEQRxOMpOFe9oQk2kTIEm6yo09fX56h5OJ1OzxM5iY/dS07MplZVNSfYNC5SEi1PsKkXUevpp5/GzMwMpJYIlNWNhozhu3MFhKAXg4ODeOONNwwZwwp4SR5Py8LXjKeVzTH1HN1dCr4f8jWXzgb1NQUKjnNxcXEpl66uLgBAU6sAxbO4W3/FaiZinDp1ynC7rODAgQOYnp5GNEqwavXyJA5KiVYW7d1339XTvKpwBRuXuiKZTOKVV14BAARpy7LOEaTNAJhgU+8laXiEcinBhpdJc2oUMzC/6awouIKNJthI851AsuwKNvOyJRZ4zSnw6EKxRP8aAKCiAioqBcc6jXmZfB5nzzNciBGzwkwxQjBacJzT4AID9eWe29TnK3ivnpmentYc6fm/QaWQ7Gfr8TebmprKlY2LLF0SDR4F8ChQVdVR99XAwABSqRSQF9xFRAHEx8Q8p5V37e/vZw3SCYEQW6QkWjwMEIKRkZG6CCxQVRXPPPMMANa7xqjSQ0SgCLxvHQBo49U6iURCi9j2r4wueJxvZQwAcOzYMTPMsg2aYNNU+lnFhRw3w8bFxaVSTp48CQBYsWbpEmArO9kxXOSpN9555x0AwH33i1U9wx+4n/1OvJ+dHXAFG5e6YseOHRgeHoYAGT4aW9Y5FBKCRHxIJBJ48803dbbQPkxPT+Ps2bMAAI84f0PvEdnC++zZs46KuMynWLCRRF/J152CqqrapkJS5jvgeYaNkzcepcSZiYkJTE9PW2CN9fDFpBQs3U+MEAIpwObqel1ELkYmk8G1a9cKXhOy2WvFrzsFLcJ7AcGGCzm9vb2YmpoyySr7wJ3IhYIN+39eKqye0QQWUQKR5cUPXgTqZ79ZPT7Pz5w5A1VVQXw+LWNkMQghEBrYPFyv0Zel0EpxBgudqSQUKHzfIXBnutAUAREXLq9CREHLwDl+/LgZphnKuXPn0NvbCyIK8N7RYehYvntWAQAOHz5cFyUsT548iVQqBTGoQMr2qSmFbxWbX86dO1f3wZCc2dlZbR23YIZNs1vNwsXFZXlwsbxjzdIlwFZkj7l06VLd7Z1UVdUEmwcfXF45NM4990oQBObLssu87Ao2LnWFVn9YaCnZL6EcCCEI0baC89UjJ0+eRCaTgUi9kIQSzcCpByL1IJ1Oa05Xp7GQYOO0qEvOrVu3sg95AkmeL9jIbkm0+VkRhDk96iEKtVJSqZS2mJQDDQseJweYmONEwebWrVvzeh4JXuc430vBAwl4Jk0xVPFoPVuclA3A0fq3+HIOICcJNlrmSLC0A6xcSKB+nfJcdKHxhSPei6Fx5wk2vCwwDRUKNjTszB6OXLAR2xZ+XnP4MUePHjXSJFN4++23AQBKZxOovLxmxeUihn2QWqNQVVUbt5bh+0PfqtiiUc1S0AM56oOqqo6ZY3p7e5HJZCB4RMghpeQxbkk0FxeX5cJLTHasWvq5FQhRhKMUqqpq+6x6obu7G/39/ZBl4K67qnuG+3wEGzeyc9jlGV3WN/rYxz5mtB0ghGjKmIvLchgfH9fS10K0tapzBYUWDKUv4ODBg7h+/Tra29v1MNFW8E0Wz6QphhACjxjF5Fw/jh07hve9731mmmcLijNFRMnZC2uthJ4cAKXzoy95SbTBwUGMjo4iEomYaZ4tKC6vI8p+pGbH0dfXhzVr1lhklTVcvHgRMzMzIIII0RuCmkmVPE7KCjYnTpww0zxbwOcS6vEjk2ART6KP3UdOcxYChT0UxNDCzmYxGEVyth/nzp3DfffdZ5J19oBfF9SfEywEPxPQh4eHMTExgWBw4ebPtY52zwSX7s2yGDT7G9Xj85yL37Sh/ExzLtg4STjnZThpJIzMtVxQBY2y7BGnZQvzbBmxtXRGbD5iaxyzxy7URYbN1q1bAQDe28zZ63lvb0eyfwRvv/02/uN//I+mjGkUvH+Nty2y5LGetjDmRqZx5swZfOADHzDYMuvJL4e2kJjFM2x4/zCvd+mMyHrEqDKE9US99Atz0YeJiQktg6+9DMEGANpWihgbmcPp06fxwAMPGGmeqfBn+N33iJDl6ueSBx8S0dWVwtatW/EHf/AHVZ+vWsr6192+fbuhE6mqqu5E7VI1b775JpLJJGQSgEKrc1ZIxAMviWJGHcErr7yCL3/5yzpZaR+4YOOVFnaKeaUYJuf66yKCrlKmpqbmZdgoInOQXbx40ZHzFo9ml5XSNfGpIEGU/Eglp3D+/Hk8/PDDZppnC4qzr0TZpwk2TuPIkSMAADnYCEIIFtpqyCHW4Pfs2bOYmpqC37/8vhS1htavxR/GXJFgc+HCBcfNM319fZicnGQ9FAIhIJMpeZwQiiI52F93UWLloGUFBHKCBZEkEMUDdTaBK1eu4K677rLKPMPJCTbVrfNoqH4zQrn4TRuWdrxzaCM79uzZs5idnYWilI4Iryd4EAqNFl5LXLBxUg/HdDqtZcSKLUsLfUL2mNOnTyOTyYDS2iza0dfXx0RKQuC9rc2UMb23t2P83S7s2bMHk5OTCASqyxa0Eh7h7W1dWkD3toUxfqpfE3nqHb4+8bcs/O8r+WVIARnJyTmcP38e99xzj1nmudQYTtoLuCwNz1SMxil8/vKevx2rRXQfn9Oe9fUCz4R5+KHqyqFxHnpIwk9+nMCRI0cwODiIhoals46NpOzVlaqqhv1xcdGDl19+GQAQpC26nC8osCydl156qe6uU1VVtdIHXmnhjZknK+YcPXq07n6DpdA28iRXI1+SggAIJicnHdkQXBNsPJEFj+FijhNLFQHzy+uISv2W3VmKfMFmMUTFD0HxI51OOy7LJifYRLTXBF8QIBRTU1OOK7/IHTlCMAJSIouPw7NvnOL44aRSKa3smeAvdAJxAaceBYh8uIOQVpnBScPs8729vXXVU2FycjKXOdJYvmBDAn7AoyCZTGq/cb3DM2i4QMOhsVyGjVPWvj09PaxfpSSCxpYWQ4V4CJAETE1N1fScwyNzlRVxCAGPKWNKTSGIsQDm5uawc+dOU8Y0gmQyqe2VPOVk2LSy+8op8wv/nsEVpYPcAOaED3awZ3e9OVFdXFyMg5ejXNlZvkjB+9jUU6uDvr4+dHd3gxDg/gf0KWkaj1N0dgpQVRXvvvuuLueshrK+lR0MdXFZjL6+Phw4cAAAK2emBwHahFs4g4sXL6K7uxt33HGHLue1A9euXcs2uyRQxDCwQOy7R4wAIBgcHERfX19dloZbCL7QVuQIZmZZI3lKBMhSCHPJMZw5cwatrdWV3qs1uHNUWUSwUTxRTE/2Oc6RCjAhlEe/cySFOT6KX3cChw8fBgAooaYlj5WDjZiZncKRI0fwwQ9+0GjTbIMW0RyI5F6kFKI/jNTkCLq7u9HRYWwTZDvB5w1xgf41nHzBxklZSNevX0cymQSoAOIrbPAsBINID92s61J6c3O5yEAhXl3EG/V4QPx+qFNT6Orqqpt5p6uri90TAT+or/zyOoQQCI0NSPdex4kTJ+q+1ODMzIwmiNNIoUBBQgGAEkxPT6O/vx9tbeZkXlgJj9YVmyIgZWTLEEohNkWRuj6IU6dOYe3atUabaAivv/46AMC70bznLCEE3o3tmNhzFq+//jp+7dd+zbSx9aSnpwfJZBLUI0KO+pY83tuWC+hKpVIQRWP7BVkNf1YFOhYWbPj7w2cGHS3YvP/97weAmp1HzMApwQMu5cFFlxWd5c+jK7PHnjt3DolEAh6POUEKRsKDHtZvEBAM6pfpe9/9Inp60ti5cyc+97nP6Xbe5VDWv/Av/uIvGm2Hi0tVaAtuEoFE9Jl8BCLCTxswmbmJV199ta4EG55d4xHDoERARi3dW4ISAR4xjERqFMeOHXOUYMMdhx4lJ9gAgEeOYC45hu7ubvzyL/+yVeaZTiaT0XpLKMrCzlSefePEUkWDg4OYmpoqeE3y1G+fhMW4ceNGNquIaCXPFkMJN2Nm8DIOHDiAP/qjPzLeQBuQSqU0YVgKFmY6SqEGpCZHcPLkSfzqr/6qFeZZAv89hEX61wCAEIgAhGBsbMwxDlUgr39NIDhPpHJChs3Zs2eZYCXLIDqUERLiDUhNTeHYsWN1I9hwx3sl/Ws4tCGGdO91RzQFv3TpEnOAeRQQb+G+gQgUJByEOjKOnp4eR8wvmhDaXP51I2QFm9OnT+Pf//t/b5RphnHt2jWWCUwA310rTB3bd9dKTOw5i3fffbdm+47xa8bbGgahSwdNyDE/qCJibnYOPT092LBhg9EmWsbY2JiWDcszaBaCZ+A4WbC5++678bOf/QwrV6602hSXGsEpgVoLwStSVJJhE4lRhCIU46NpnDp1Cg8++KBR5pnGjh07AAD33qNvAMC994p44flZ7N692/IAg9osOOviUsQrr7wCQL/sGg4vr/bqq68is0At/VqE96TxLNK/huMR2TG8vJFT4I5DWS6MjFLkCADnLayvXLmC6elpEEK1rJFSKJ5c5Hs6nTbLPFvAS0MIcs6RKHrY9XPx4kVH/R7vvfceAEAKxEBFeYmjmWADsKyc2dlZQ22zC+fPn8fs7CyIKLEyaHlIIVbKqJ7S1suBf18xsngpJyIIEIKRgs84AZ6pJwTmz8FcsKnnbD4ebCLEG3TZrAtxdp3VUylGTdQLLx7VXQoaCReco57ReiFFSq9naITdT7xsWr2jZXs2Rcr+jNAULfhsrfHqq68CAJTVTRCC5jZ7l1oiEBtCmJubw5YtW0wdWy+4sOttj5R1PKFEy7Lp6uoyyixbwO8JT9wHyb/4Gji0kv0mZ86cQSpVOoDSCXzwgx90VGCoi8tyGRoawrVr10AIsKqCDBtCiJZlc/z4caPMM41EIoHdu3cDAO69T5/+NZy1awUEAgQTExM4ePCgrueuFFewcal5Tp8+nXWuEwTo0qV3KsFH46AQcePGDezbt0/Xc1sJd6b6pKXrm3uzx/CSc04gk8nkMmykSMF7HqW2N6jLhTvKFG8chCz86JCVEAgVMTU1pdXRdwpajx9vzlEmyn4QKmBubk6LtnMC+/fvBwAo4fJEdNEXAZU8SCQSdeU8XQwuNEjBOFDkfM4XbJxSBmFwcFArUSSGl342iRFWEqseNh3lwnthUX8JwcZf//2y+L+1oFMDUNqQu4bq5T7jYgsJVx6xzz/jLMGmdPQ7F3Kc8FuoqpqXYbN0IBdHzB576tSpmrt/VFXFSy+9BADw3W1+VD8hBP572Lgvvvii6ePrARddvB2Rsj/j7YgWfLZe4WJWcOXSwrm30Q/BIyKRSDhu3+RSPpKkr0O61nFaWfp8uE+muU2Ax1eZO3/VOnYd1cPeaffu3ZiZmUE8TrBmjb6yBqUEDzzIxC3e684qXMHGpeZ55plnAAAB2giBLB3JXQmUCFqWzdNPP63rua1iaGhIK1fllZZ2evhk5jjr7u7GyMiIobbZhStXrmBqagqECJClQqeHIrPNxtWrV+uqUfFS8Ae7x7v4NUMIhcfLrhm+oHAKXLCR8nr8EEIgecIF7zsBTbCJlCfYEEI0cYd/tt7RBJvQfHFCDEYBQjE6OuoYoY8LdUIgDCot/SyXnCzYlMiw4Vk3IyMjdfts0gSbKvvXcIQYE0tv3ryJ/v5+Xc5pNTzDioYqF2z4Z4aGhur2GuK4GTY5ent7MT4+DggUQkP5mVlCQxigFGNjY5rYXiucOHECFy9eBBEF+O4wtxwax3fPKgBszVNrv18ymdRECV9H+SKfb0UEQP0/t/lvE1qx9P1EKNHKpjktGNBlaf7zf/7PUBQFjz76qNWm2Irf//3fx4c+9CF885vftNoU01lOOTTOqrVSwTlqmbfffhsA8NBDkiEl8h5+iP1WW7dutTQoRRfB5itf+Qr+5E/+BN/61rcq+ty3vvUt/Mmf/Am++tWv6mGGiwOZmZnRyqGFBGPSaMPZ87699W0MDQ0ZMoaZ8OwaWQhCpMqSx4vUA1kIFny23tF6/MjRedkkoqBAEv0A6uNhVy6HDh0CAHh8SzvKuKjDP+MUeI8f2VsYsSt7IwXv1zt9fX1ZkYFACTWX/Tku7jhFsOEOCyk8/54iVGCiDerfscHhpTrFaHnOeDHKeiOdPHnSMWVErl27BiCXTZMPkWQQmT3Ta835Vw43b97UnOx6ZdgQUQSNsvusHtY3iURCE55oePG+CaUgsgxkm9DWc2k9IE/8XECw4dlGThDMebaD0BABEYSyP0dEQRN4aq005XPPPQcA8G5sB/VYE7kuRvxQVrPKEC+88IIlNiyXrq4uzMzMQPDJUJrKF4f9qxu0zxf3fKwn+D1VToZN/nFO6B/mUhlf/epXcfDgQXR2dlptiq2IRqP46U9/is985jNWm2I63P+0al3lfVVWrGGf6e3txfDwsK52mUk6ncY777wDgAk2RnD3PSJkGejv77dUTK9asNm2bRu+8Y1v4P/8n/8DSis7HSEE3/nOd/C///f/xp49e6o1xcWBbN68GRMTExDhhY9U3mC1HBQahEJCSKaSNZu2ng8v7eaXl24EzvFlM3H27t1riE12QxNslNJlefjrTnGkDg0NaSXivP6lHfDeAHO87927t+bKZCwXVVW1DBrRW7hBkxwm2HDBRQ7GQcXyF1E8w+bo0aN138dmfHxc65MlhUuX8pQj7HWra+eaBRd4pVh5Ip8QjICIEqanp7X5qZ5RVTVPsFkgKyAr5NSjk3nXrl0AABqPgyhLB5uUi9jaVnD+WubKlSvsmSvLgGd5vxHNChVcHKtX+L1EgvPFTwCgIRaY09fXV/f95/gzW2xfuhRlMWI72x/UkuA5MTGBl19+GQDgv39NRZ9V0xmkRqaQGs2JDanRKaRGpqCmK+916n+Ajf/000/XVOABX5f4O+MgtPzIZjnmgxT1IZ1O121v1ImJCW3+DJaRYZN/XK0Jny7GQwiBz+ez2gwXm6CqqjZPLCfDxuenaGplgRm1PN8cO3YMIyMj8PmB224vP9CkEmSZ4O57mMDFxSErqFqw4dkNlFL8zu/8TkWf/Z3f+R1N5KkHR7iL+fAIqZDQakgqHCcksA39888/X/MOaC66+KQKBJusuFNPfXwWg0d6excQbPjr9brZKEZzwHsiEEXPksd7fY0ghOLGjRt17/ThXLt2jZUUIRSyUrhBk33O6nvE55hy+9dwRG8IVPZibm4Ohw8fNsI023Dw4EGoqgrBF4LgKb0Rk6Ps96slR9hymZub0yLGxFh5vegIIdqx9X69AMDw8DCmp6cBANTnL3kM9dW/YMMFFr0QsufbvXs3MpnKHa52QivzFQ4te03MM3PquRRYIpHArVu3AOSEmWJIwAcQgmQyiYGBATPNMx3+zJZWVfbMBgBpdS5Ap1Z4+eWXMT09DbEhCGVNZb1P0+Mz6P8/r2Pge29prw187y30/5/XkR6fqdgW3x0doD4FAwMDePfddyv+vFXw/WCgs/y9JCewlol89ZpNzdcj3kYf5GB5wnl4DQs6PXHiBBKJhGG2ubi41DZXr17F6OgoBBFoW1l5hg0ArOhkn6vlSjG8HNp990kQReN8wA8+wESxmhZs+MP2zjvvRFNTZYue5uZm3HXXXQCc4wh20Y/e3l7NkcUFFaMI0mYQUJw/f76mGyX29vZmy1wQeKXyI+lYhg3BpUuX0NfXZ5h9dmBiYiKXTeIpXXbF62EblCNHjtS8g6ccdu7cCQDw+ctr8EepCI+P/Ub1ELVcDryMgeyNgBRlmyo+dq9dunSp7vsCpNNp7XpRopWVqSSEwBNlc/n27dv1Ns1W8DWPHF04m4S/d/HiRdy8edMUu6yiq6sLs7OzILICwV9+KSeejXPgwAGjTLMNvBkx9QUWLFtEg/XrbOeZr0JL5U7lxRAaGgBBwPDwsFYmq1bh/+48S2Y5EAcINlpjb2XhTCRCKUg4UHh8HXL9+nVcvnwZIATiysr28QAgrmgCCEFPT0/N7A9effVVAEDgobWGBvuVAxEFLcuHB8HandnZWS3DJrB+GYLNenad1ZLIVwl8PRJZV/4+29vogxL2IJlMakGDLi4uLsVwkaVjlbhsoWJVZ233sVFVFVu2bAEAPPTg8kSrcnngQRGEMD8Pz8w2m6oFmwsXLoAQgjvvvHNZn7/jjjugqiouXLhQrSkuDoNnZXlJDBJZOuq/GgQiwU/ZopRn9dQi3HnulWIQaPlplAKV4BFZlgB3xtYrx44dQyaTgST6IYmlI989cgSEiBgfH6/7RvKZTEaL+vMHy3fA82O3bdtmiF12g6cVK/75GzRB8kCQ2bVU7/WpT5w4geHhYRBBghKq3PnjiXYAQE1Fmi6H3bt3AwCU+MLBBlRSIAbZ9VTvZWN58I8Ua67IgSbFc4JNvYvnmmATWrjEipB9r94czKqqaqJlqf491UAEAcTL5ufBwUFdz202/PlCopFln4PG2GfrOSP07NmzAAChIbrofCM0RAuOr0d44JvQGgNV5Io/Tz0yhJZYwbnszOzsrOak8m4wNtivXLwbWDDUoUOHaqKKw9GjR5FIJCAGFXhaKu+VFVzH9tNdXV0YHR3V2Trr4fdBdH35gg0hBJHs8bVwH7m4uFgDL8e/nHJonFVrJe1ctfDMKebcuXO4cuUKJAm4735je9CFQhS3Z0uucZHIbKoWbMbHxwEAoVDlD2wACIfZ5nJsbKxaU1wchKqqeOmllwCwcmhmwLN4Xn/9dczNzZkypt7s2LEDAOCvoBwaxy83FZyjXuGOUZ5FUwpCKHzZ7BvueK1Xjh8/juHhYVAqwesv3wHvDzLH+4EDB+o+qwTIlcdT/KWzsjz+xoLj6hUutHiibfMyjcrBE20DCMvmu3z5ss7W2YO+vr5skAqBHFv8+aU01E9/jcXgDgqpocIyepE4IIgYGRmpe/GcBzYJwYUFG5p978KFCzW5CVuIiYkJbd1FPPoH6PBz1rpgwwMHhMbKe5FwhIb6zwjl/cNoPLLocfz9ehavtLl3ZXm9w0ohrWgqOJedOXHiBJLJJGjAAyFauhye2chtMUCguHXrVk1k+fF9YHB907IylKSwF57mEFRVrbu1zdjYmDYPR28rvR9YiOhtbO6t18wjFxeX6uEZeKvXL1+o6FgtQhBZqWVWeae2eOstVpL07rtFeDzGZ8k+/D6pYFyzqVqwCQRYpNtyBRf+OUXHBqIu9c/Ro0dx9epVEAgI0MqjuJeDj8QgQMbo6GhNihbj4+NadoxfqbykSEBmm7kdO3bU7SYeyJViCngXj7zze5mjtd4zjniGjC/QBkLKf2TISgiSHEQymax7USs/YlMJlJ6P+Ov1LNioqoo33ngDAOCNr1zWOagoQwmze4ufq97gDgop3AAqLb724Rk4u3btqtvG1/k9i6R4Zc8mQgVI2T429VoPn8NLddJwZMFjWIYNwcjIiNajox64ceMG+x9JAhH1L39Avd7CcWqQoaEhrSQVbYgt+zzE6wEJMEd2LZcAXgz+vWhjdNHjaCP7Hes1M1ZV1Vz/mhXL30vxUmp79+61vVDMS3kpKxosL4fGIZIAuY1di4cOHbLYmqXhgTnBjcsvT8k/W2/Z1Pv370cmk4GvOQBP1FvRZ2O3s8Cu48eP1/U+28XFZXnMzMxo65E1G5Yv2IgSwco17PO16Jfgwsn73mdsdg3n4Ydzv5UVe6uqBZumpiaoqrrsepv8c5X2v3FxNjy7JkCbQEnpWu56QwhBUGgpGL+W2Lx5M5LJJGQhCEWoPCNOEcOQhQDm5uYsSwk0mmvXrmmR737v4hsRLtgcOHAAk5OTJlhnDVo5tFBl/UiAXJZNvZdFO3XqFGZnZ0FFBZKn9L3lCeaao9er4/3UqVO4fPkyCBXgia9Y9nl8jasBAK+99ppOltkLLgorDUvfU1K4CUSUMDIyUrO1hpeiq6sLiUSC9a8JRir+PBd56rmPjaqqOHfuHABACC/sZCaCqPWx4QJPPcC/+2JiVTXQbLY/H6cW4VHdJBwCkSsvbZUPzWbo1OOck06nNYcHbVo8E4m/39PTU5cO1NOnT6O/vx+QBIgdlWfec6QVTYAooK+vz/bzDhdElFWVZT8YjbKS/f5cULIrvb29bJ9ECYK3LT8rK5QVbHbu3IlUKqWXeZbDA9RiGyu/n7xxH7xNfqTT6boPQHFxcamcrq4upFIphCIUsYbq3Phc8Km1nlmXL1/G2bNnIQjAAw+aI9jE4xRr1woFvXPMpGrB5v3vfz8AtsmpNCrkwIEDOHv2LAgheOihh6o1xcUhzM3NaZHXIUHf5rNLEaLMSf/uu+9q5QBrBd5kM+TpWFZUGSEEIYU54GulMWalcHHCqzRAEBZ3eMhSEJIYQDKZrNv+ErnNN4E/sBzBhn1m+/btdStSALnIfk9w4f4bsi8KIkiYmJjQyrHUG1xg8cQ6QIXlL6K8DasAQnHu3LmadqCWYnZ2VotoVho6ljyeUKpl2dRbJCqHrx2l2DLLq8RyYqjdo7uXy40bNzAyMgIQovWpWQghK2rY3XFaCVrPkUjEkPPT7HlruVcJL9tVTXYNh5+jHkuB9fT0YHp6GpBE0OjiwUvU5wEJ+qCqal1m2bz99tsAAGl1K4i0/Mw1IomQVrcUnNOOpNPpXPnaVcsXqIyA22P3wIN33nkHAOBfHYfoW74w7F8dg+BjVStqMcK7FKqqahU44ncs7/qK3+GM8uMuLi6Vc+zYMQDA6nVS1Rmiq9aJBeesFbhgcscdIgIB87JkeTZPTQo2n/70p7X///KXv8wWwWUwNTWFL3/5yyXP4+KyGLt27cLo6CgEyPCS6jemlSCTAGTix9zcnGV1DJfD4OCgVls6qFTueOcEPczBuH//fgwNDelim53gmSAB39KNSAkh2nH1mkHChSiPNw5BrLxspdffBEpZdkA9OQ+L4feWN7iwgEwIhSdQv6WbVFXV5kRv45qqzkVFmfWygXX1Yo3iwIEDmJqaApW9EIPlPb+UBpatVO+CjRhbXrSuGGkAKMXg4GBN1mIuB80ZHwyDCIs7VoVI/TnbefYINUywyTWXn52dNWQMo+GCghDXQbCJ1981xNGyaxqiZfVZ42XR6vG34GtXed3y9wUcfg7u0Lcjx48fx+TkJKhHhtS8uPBtNsrKOEAJrl69it7eXqvNWRD+7xu+q7r+sUSgWpaNna+ZSjh//jz6+vpAJYrohuX1EWu4k+0Ttm/fXrcBKC4uLssjJ9hUXxp49TomQJw9e7Zs/70d4ILJww/rXx55MR7Kjvfee++ZHrRftWDzW7/1W1i/fj0AVtftYx/72JIRamfPnsXHPvYxHD16FIQQrFmzBp///OerNcXFIfBMkaDQYnr9YUIIgrSlwI5aYPPmzchkMvCIEcjC8ptsyoIfihhGOp2uO0fq5OSk5kgvR7Bhx7EN6rvvvluXGST89/AGlpfJRgiFx9dYcK56I5lMatGBnuDiDmf+vt0jKJfD+fPnmZOBUE1sqQbeA8fO0brLgTsmlMbyMx1Z6TSC7u5uXL9+3UDrzCeVShVk2CwHIggQw6y8TS00vV4OPCuPizGLUW+CzdTUlPbvKrQs/CxSMxlkJieRmcqVKM1MTSIzOQk1k1l0DBoKgXi9SCQSNfus0kS9+OJ9WcqBiz6XL1/G1NRU1eezE/x3EhrK+52EOs02unHjhiZeSWurf2ZLnewcXV1duHnzZtXnMwKetaCsbS5LrDMT6pGhdDAnv12zK0ZHR7WSbaE7qhNsACB0JzvH1q1b60Kc4P9u0fVxCPLynImR9XFQieLGjRt1l2Hu4uKyfFRV1fZLq9ZVXwosEhMQiVNkMpmaKYt28+ZNTbR6sMxyaOm0ilu3MhgczO0DBgczuHUrg3S6/OdOa6uA9naKVCplegBl1asVSil++MMfQsw2AT148CDuvPNOfOITn8A3vvENbNq0CW+88QY2bdqEv/3bv8UnPvEJ3HnnnbmISlHED3/4QwiCOX1IXGqbiYkJzYHHhZNyUNUMkuoMkuqM9hr/u6ouvpEvhvex2bdvX800qH3zzTcBVJddw+HnqLeG4Lt27UIymYQkBiBL5fX48XkaQamE4eHhmkspXQpVVTXHlc+//DrVvqzYU6tOsKU4efIkZmZmWP8ab2TRY3kGzqFDh+pO4OPzsifaVlU5NI4nxrL5Tp06xWrs1wGqquYJNuX3+KGyB1KECZ/1EonKOXHiBMbHx0EkGWJ4eRGpACA1MsfPrl279DLNVvT09AAAhFBkyWN5j5srV64gmUwaaZYp8GczCQZBFykHp05PY+rlFzGd1/tq+rXXMPXyi1CXiB4khEBsZ3NOLd5jIyMjWlQ+1SHDhng9ID5WCqyrq6vq89kJnu3LM2eWgjay+6neSpnyXmpCaxzU56n6fDTghdASKzi33di5cycAwLu+erHBCDzr7f0ce/fdd5FKpeBpCUFpCFR9vuBtzSAixdWrV+tCnOD/brE7lt+XWZAFRNfHC87n4uLicuHCBQwNDUGSctkx1bJ+IytrWSvBbnx9vnatgGisPBljeFjFH//3CfzP/ycXzPU//59J/PF/n8DwcGWBAg89zH53s4NJdQkv+chHPoKf/OQnUBRWMieTyeDtt9/Gn/3Zn+ELX/gCfv3Xfx1f+MIX8JWvfAVvv/02MpkMVFWFoih4/PHH8Uu/9Et6mOHiADZv3oxEIgGJ+KCQYNmfS2EWl+f24Goy5zS+mtyPy3N7kEJl5S8k4oWHRKCqKl5++eWKPmsFg4ODWkRUUKk+io6f48CBA3VVFm3r1q0AgKCvvezId0IoAt62gs/XC6dPn8bAwAAIEbQsmeXgC7AN6L59+zAzM7PE0bWH1r8msHD/Go7sj4FQCePj43Xn/OEpyp5Y+ULEYgiyF3KoseDctQ4XnwgVocQqcxh5sgJPLTqTF4M7JKSG1qoinuUmFkiwd+/eumpgzOGl3mhw6XUP8XgBQUA6nUZfX5/RphkOL9skdqwwNKta7MjdY7UW7c2zPEk4BOKpvHxpKWhzQ8G564ULFy4AAGisvJJY/LhLly7VVaAFjxCVdciu4chrc1nndmN2dlZbd9mtfw1HWc3sOnHihMWWlIavxcJ363PNCIqI4IamgnPXKtPT09pee7n9azhc8HEFGxcXFw4XVdZskCBK+qyF193BBIhaCarlvjazy6FxHnqIjbtz505Tyyfrlg/8+c9/Hnv27MEHP/hBACySdKE/APDhD38Ye/fuxX/6T/9JLxNcHMALL7wAAAjRNtPLoeUTElo1e+y+sd+6datWDk0SfFWfj5dFy2QydSNSJBIJzREa8C/dCDyfgJ9tUN98801klii7Ukvw38MXaAOly38wykoEouRHIpHQeuLUE/x38obL6XtE4Q3ZvzFvpfT09LAobELgbVip23m9DasBAK+88opu57QS/m8uN7Qt2YekGKWR/a5W1M41Eh7xzAWX5SJG4iCSjPHxcRw/flwP02yDqqq4fPkyAEDwlyHYEAKaPe7SpUtGmmY4qqpqzw2xTT/HcimElhZAEDAwMICLFy8aOpbecFFFaNbPEc3PVU+CzcTEhFaui0bLy6QmQT8gUMzNzeHatWtGmmca+esxaW31mfccXlpt9+7dtusFdf78eaRSKVCvDCFS/V7ICKTmCEAIbt26ZbuycjMzM5qAEL5Lv7mYn6vWBZv9+/cjmUzCE/PC11xd9lF8I5t7ec9DFxcXFy7YrMtmxegBz7A5ceKE7fvYTExMYN++fQCABx/SJ8OoUjo7BcRiBFNTU9i7d69p4+pawPX+++/H7t27sX//fvzlX/4lPvnJT+L+++/HunXrcP/99+OTn/wk/vIv/xLvvfcedu3ahfvvv1/P4V3qnOvXr+cyRYTl9dTQiwBtBgHFxYsXtRrQdmXz5s0AgIAO2TUcnmXDz13rvPPOO5iamoIo+uBVGir6bMDbBkpEXL9+HYcPHzbIQvPhzmV/qDIBqxhCCPzBjoJz1gs3btzQnMO+aHmZJb4oc7zXi9gJ5AQVT6QNglR9aRWOr3ENAILjx49rDutaRstCqqAcGkf0hyD6w0ilUlrGQa0zMDCg3T9SlYINIVQTfeptnrly5QrGxsYASkGD5WUFCBFWxsmukdrlcvHiRQwMDACCAKFx+WVmyoEIAoRG5igzcyOmB1xUoToKNrSJnevo0aN1E4zCyy4RvxdEKc/pQSgFjbL7bqkerbXCvn37kEgkQIM+CE0R3c4rNEdBAl7MzMzYLmKX/9tJLRFLA/4Wg8oixAYmtvPSfXZh165dSCQSkKM+eNrKew6VQ+jOVoCwkoO8rGMtwtdl8buWzrZfCl9LAN4GH5LJZF0Gurm4uFSGqqo5weYO/cSKWCNFNM76stg9OIeL4s0tFO3t1rRSIYTggQfY788DDs3AkI5773vf+/DVr34Vb7zxBg4fPoxz587h8OHDeOONN/DVr34VDz/8sBHDutQ5PLLHQyKQiH5OweUgEBE+ymrMmnnDVsrw8LC2adKjHBonKLNz7du3DyMjI7qd1yp4abuwf1XFC21KRQT9KwrOU+tcv34922CXwB+sPvoykBV9tm3bVlclRbgDXvE3QpS8ZX3GF+kAQHD27Nm6ECFUVcWrr74KAPA1dep6bkH2QomwbMbX8vpS1CJnz55lzkJCK+pfk4/SvBoAtN+71uHZaWK0EYJHh+zPFiaGbtmyxfaZr5XAm4EKkThImf0exVjO2V7LcOFEaGwq+7tXg9DC5ptacpLNzc1pwpyeGTa0IQYIAkZHR2s+U4uTE7YqC8yhzWy9X+v3E4eL2tJafasVEEK0smh2E84nJ1n9eurTp2SgUQhZ+7i9duGtt94CwMqh6XnNiH4Fgc7GgjFqjUwmowk2jfcsv+cnhxCChux56q0MrouLS+VcuHABw8PDkGRgVad+gg0hRMvYsVuQRTG7d+8GANx7jzXl0Dh3Z8fn9piBIYKNi4sRcMHGR6tvqKoHXLCxc43ZrVu3Ip1OQxHDkAW/bueVxQAUIYR0Om27TVml3Lx5Ezt27AAAhAKrl3UO/rnXXnutLvq0aA3kfY0QxerFUa+/GVSQMTw8bPsIjkp48803AQD+2KqyPyOIilYW7Y033jDELjM5d+4c669BKDxxffrX5ONrXA2g9jOSeBaS0tAOKi3PYeRtWQOALRLroX8YFzzlFn2uG6mpHaAUV65cwfnz53U5px3gTmIxXr4zXsgee+zYsZrOjjh27BgAQGgyNruGIzYxJ9nx48drRvTr7u5m5acUGSRcXpmvciCUgjaydW69PLe10nEtlQk2Qkv9lIfLX7fL66vLoC6FtD4n2Nhp7uEl2ohoTWRuuRCRuWbm5uYstiTH3NycJkiEdOpfkw8/Z62WRTt9+jRu3rwJQREQXR/X5ZwNd7N9wrvvvmur+8jFxcV8eCkwPfvXcNZnM3b4GHaFB1Ldfbe1gs2dd4qglJWcvn79uiljuoKNS02QTqc15ZcLJVbDhaOjR4/aLhKKwzdlQbmyBtflwEus1boj9cUXX0Q6nYZXaYAiLy/N3+dpgiT6MTk5WRdl4rQyelWWQ+MQQrVMnVqNoCtmYGAAhw4dAgD4o+ULNgDgj60GUB+CDb//PdE2UEH/mrKe2AoABKdOnarZBuqqqmoZQt7W5Wchif4wxFAc6XS65q+doaEh7ZmutOjT94iKEqRG9lyq9d8nH142ToiX72QWwlFAEDAxMVHT2RFdXV0AACFuzrqPRqMAIRgcHMSNGzdMGbNaeClWoalR91JPPGOnHsq9qqqa+61aK8tE4sefOHHCdr1ZKuX48eMYGhoCUSSIK/UXQqVVzSCyhFu3bmmCqx1IJBIAACLZXbBh9nF77cD+/fsxMTEBMajAv0r/uTiSFWyOHDliu9495bB9+3YAQOz2RlCdrq/IuhgEj4jh4WHtOeji4uJMuFhx+1369a/h3JY958mTJ23bI3V4eFirSnL7RmsFG5+PYPVqNs+btcZxBRuXmqC7uxtjY2OgEOEhSzfdNQOZ+CDCi1QqZcvN7OTkpJauF1D0F2yC2XPu3r27ZpsiZjIZbNq0CQAQDi7fkUoIQTjAPv/MM8/oYptV9PX1ab2iAqHKhIjFCIbZuV577TWkUindzmsVb775JlRVhRJohKhUlr3mi64ECMGZM2dqrrl1MVyw8cb1cboXI8geyKGmgrFqjWPHjuH69esgggiloToRlGfZ1Log8frrryOVSkEMxyEE9KuHr7Sx3+fll1+umQyJxZiZmdF6L4ix8gUbQimECHOsccGn1piYmEBPTw8AgMbMEWyIKIJGIgDY5rUW4OtPPfvXcHgfGzuucSvl0qVLGB4eBgQBtKmyTH0SDoD4PEgmkzVzXSwEf45KnW2GlBkkggCpk+0P7JSBzx1R1GNNs+JyIVn77OQ445kv4bvaQKj+/X+ksBe+VeyerMV1Hq/SEL9TPwGUChSx2xoKzu/i4uI8UqmU1r9mgwGCTSQuoLlNQCaT0caxG7zsb2sbhd9vfQ+6tetqTLDZuXOnbn9cXBaCR+J6aQSE2Edn9FHW2NeOE9z27duRTCYhCX7Igv4ilywEIVEf5ubmtOiiWmPv3r24cuUKKJEQ8lfncA4HWYP0Q4cO1XRjWl66yetvhiTrV0bPF2iDICgYHBysuYbOpeB9RHi2TCWwsmgsorCWe7Ncv34dp06dAkDgielfWoXjbWAls2pxIw8wcQIAlKaVIEJ1kUGebB+bgwcPor+/v1rTLIP3+1I69O17pLSw37i3t7cuyhedPHkSqVQKxOMF8VY2H4vZjJxa/R14VDHx+0E95vUtFLLiEN8g2pl0Oq2VsRBaq++dUIzQ0ggQgkuXLtVshiOHZ8TS5vJ7QXEIIaDZLBse0FKraP1rDCiHxuHntlP/jYmJCQD2F2yohznk7CLY5PdnCd+lfzk0TvhOJvLZ6Zoph5GRES0oIn6HvqI5F4BqdY/t4uJSPadPn8bk5CS8PoL2VcZkl6y7gz137OjPBHLCyNq19siQ5XaYFRBX9b/6L/3SL+mSgk8IqYuoaxdj4BOIl0QttqQQL41iPNNnywmOO8SCir4NIjmEEASVdgzPnMcrr7yCT33qU7qPYTQ///nPAQCh4GpQWt10KIk+BHztmJy+hp///Of42te+poeJppJMJrUMoWB4ja7nJoQiEF6FseFzePLJJ/HRj35U1/ObydWrV7MPabIswQZgQs/M2HW89tpr+O///b8bco8aDRdQ5FATBNlr2Dje+EqM9RzCwYMHMTw8jFjMHn3MymF2dlabiz3N1d9TgscPKdKM5OgAXnjhBfzRH/1R1ec0m/Pnz7P7hxAo7TrPM6IIuXUVZq9dxHPPPYcHH3xQ1/ObDXcyiw1NFc8RYkMTZs+d1s5Ra3DBxKxyaBwajwMXL9REJoVWwkKWtH4zekIUBbQxjszNQezevRuPPPKI7mOYxYEDBwAAQtvynKpCWxPSF3tx4MABfPnLX9bTNNO4ePEiK5EoUMhr9M+850idrQCluHjxInp6etDZqa8wvxwGBgYAANS3vB5yZiH4mX3cXqs5efIkbt68CaqI8K+trPdTJYTubEX/G6ewb98+TE5OIhAIGDaWnuzcuROZTAaBtiA8MZ+u547fxQSbEydOYGhoCHGTn4UuLi7Ww9cua2+XQA3IcASAdbdL2PP2jC39mUCul+f69fYQbLgdXV1dmJubgyzrn/mUjy6pCqqq6vLHxaUUiURCm6y81H6CDcAWtHZqAj0yMoJdu3YBAEKKcVF0IQ87986dOzEyMmLYOEbQ19enRRpGg+t0OWc0xM7z0ksvadF8tcSrr76K3t5eCIIHwchq3c8fid8GgEXQnT59Wvfzm8Xzzz8PAPCEWiBKyxMq/NGVIFTEpUuXajYCnvcj8jYYUw6NI3qCkPwxpNNpLdKzVti8eTNGRkZAFR+UuD7Rqb6O9QCAp59+uiYDXX7yk58AAOTmDlBFf6HPs5L9Pq+88oqtnsvLQRNs4pWXWhGyn7lw4ULNPZ+BnGBDKygFpwdcIDp58qTt9ya8rrnQ1gJCjck+F9pbC8aqRVRVzWUidbQs6xxCO8tgOnTokK0awleCVg5tZTOIYlymCVVkrT+OXcqi8fKKUkPIYksWR4yzigjcXqvh5dCCtzWDisY5y5SmIOSGAJLJZE2VAHv33XcBAPG79c9w9ES8CK4IQVXVmvpNXFxc9INXGVp7u3GiwNrb2XrgzJkzttsvZDIZLZNl/Xpr+9dwWlooAgGCubk5U/xZVa/uP/rRj+IXf/EXl/zzkY98BPfccw882bIGhBB4PB7t/VqOtnYxll27dmFqagoiFCg26V/DkYgHCgkik8nYZlMC5Bx5ihiGIhq3OVHEEBQxjGQyqfWCqRWefPJJZDIZ+DxNUGR9eij4PM2QpRCmpqbw4osv6nJOs0in0/je974HAIg2bKw646gUshJGILwaAPDd735X9/ObQTqdxnPPPQcACDauX/Z5qCDBH2N9fZ599lldbDOTvr4+ra+BUf1r8uFj8FJ0tcITTzwBAPB1bNDNoeppWg0iKejv79ecBbXC0NCQNjd6O+80ZAwx1gQxHMfs7KyWRVmLzM3NaYKN0Fi5M4gqHtAQe7bxDV8twTNcTM+wiUQBSjE+Po4rV66YOnalaIJN++LZEmomg8zEJDKTk9prmclJZCYmoWYyi36Wn3vv3r22F7AW4tKlSyxjQaCszNsyoPEIiFfBzMxMzfaF0gQbA8uhceTsGHYoZTo9Pa2V9BMb7LWPLEbMCkoXL160/H5TVVULzAnfbVw5NCDbCzQ7Bh/T7qTTaS04suEu/QUbAIhnz+uWRXNxcR6zs7Pa+v32u40TbMJRAa0dAlRVtV1wzoULFzA5OQlFAVassEdbDEKIlmXDs3+MpOpvvX37drz77rtL/tmxYweOHTuGsbExbNq0CZ2dnZidnUVnZye2bNlSc04HF/PYvHkzACAgNNuybFCAsigybqfVJBIJ/PjHPwYARL1rDR8v6mWlDh5//HEkEgnDx9ODRCKhCUzR0AbdzksIQTTEnPg//elPkVnCEWIntm7disuXL4MKCsIx/X6TYmKNdwFgUXuXL182bByj2LFjBwYGBkBFBf7IiqrOFWxg18rrr79ecxlZTz/9NDKZDJRwC0SP8aUrfM1sLtuzZw8r6VIDnD59mi3kCIG3Xcd5RhDga2fXzpNPPqnbec3gqaeewuzsLMRIHGJMvwa9+RBC4F3LxKAnnngCs7OzhoxjNEePHsX09DSI4oEQXl52sdjEHGC7d+/W0zTDGRoa0hysQtzcEoiEUtBs2UU797GZnZ3V6noLbYtnjahT05h55iUkns/1TEs8/xpmnnkJ6tT0op+ljXFAFDAyMoLz589XbbcVcKeq0NoEsswsAUKIlp1Ta/cTANy4cUMTmuR17YaPx8c4duwYbt68afh4i8GzVahPgWDzkmhSPAAQgsnJSct/t3PnzuHy5csgIkVo4/Iy0yohkhVstm/fjpmZGcPHq5YTJ05gbGwMoldCaHXEkDHid7B10p49e5BOpw0Zw8XFxZ4cPHgQiUQC4ShF6wpjy4FtvJc9G+3WVz6/f40g2McPzAUbMwJ4TJepJEnCZz/7WRw5cgT3338/fvzjH+O//tf/arYZLjXC3NycVgKHCyN2g9u1b98+jI2NWWwNi9YfGhqCSL0IKcZvykJKB0TqxeDgoFYqyu688sorGBkZgSj6EPDpGzUWDqwGpRIuX75su4feYnCRLxJbDyoYVypD8UTgC7ZDVVX89Kc/NWwco+AR+4H4WhBa3eJJCTRC8kYwMzNTUxlZc3NzWq8jf+ttpowpegLwxFjEbq2IFPxa8TStgqBz6S8uAO3evbtmhM9kMqn9Jt7OOwwNwJBbV4F6/BgaGsIbb7xh2DhGwp3CYnPrsn8rqZk933bt2mV5tHYlaOXQQmEQydja0KXgWT12FmxOnDiBubk5EK8HJGRc1gARBNAmlpXCyxPXGnwtJqysrm+LsLKt4Hy1BM90EdsaQAPG9Zzj0KAPQmscqqpaXoGAC41So73LoQEAEQWIUT8AlmVjJS+99BIAVg5N8Bi3L+B4V0QhR32Ynp62/JopBz4PxDY2gArGuNRCqyMQvRLGxsZqNrPPxcVlefB9wO33yIYHrW+8V9bGtNN+gc97a9fZoxwah9tTl4INJxgM4oknngAhBD/84Q/xzjvvWGWKi405cOAAJiYmIECGh+hTtkpvZOqHTPxIpVKW15hNpVJ47LHHAAAx3zoQYvwtTghFzMd6t/zwhz+0fU8FVVVzGUihDbr/RpRKCAdY1hEfx+50dXWx0juEGppdw4nGbwcAPPfcc6xhco1w9epV7R4PNVX/OxFCEGpk53nyySdttUBajK1bt2JoaAhU9ppSDo0TaGXXzfPPP2/76Mvx8XG88sorAABvh/6ilugLQo4zQb5Wyn5t2bIFN2/eBFG8kFtXGToWoRSe1eze4mXpao29e/cCAMSm5TuZxcYmgFD09fXZvrxXPrzxKW0wt38NR4g3FNhhR7h4QluMzz4XWpsKxqwlEomE9u8orKouQIcLPnbrW1kOvBeJtMH4cmgcOTuW1SWueIaNWAOCDZCz88KFC5bZkEqlNMEm9rCxz2sOIQTRh9iashYCAPl+IL7RuIBSKlDENrLnUS0KxS4uLsuH997bcOfSgUvptIqhW2kMD+Yy8YYH0xi6lUY6vbR/Yc16CaIE3Lx50zY91IBc4NS6dcZmGFXK2rXMnt7eXsPXg5YWgrv99tvxgQ98AADwgx/8wEpTXGwKj7Dx00ZblkPj+CmLPrQ6Imjz5s24du0aBCIj7DHPkRr2rIRAZPT29lq+MVuKvXv34ty5cyBEQCQrrOgNK4tGsHv3bpw7d86QMfSEi3zB0EqIks/w8bz+FshKGNPT03j66acNH08vnnjiCaiqCk+oFZJHn41/oKEThIq4ePGi7erGLgQvJ+hvXm9Yo+tSKNE2CEoAExMTmvPJrvzsZz/D9PQ0xEAEctSY2ub+FUzAevqZZzA6OmrIGHrCM6M8K9dXnZ1WDp6V6wFCcfz4cXR1dRk+np6MjY1pPVykKgQbIkoQ4mx9wgWgWoAHcYltxmcJl0JobQMIQXd3t1aazW7wutnL7clSCUIzc0jy0hS1xHvvvYdEIgHi94LGqgv8on4vaAMrT1hLztOhoSFNbJM3VFfKtRL4WPv377dU4OKlxcSw8etbPeB2WlkSbefOnbh16xZEv2JKOTRO9CEmDu3Zs8e2cy8A3Lp1S3Mkxu8ytgII74/DK464uLjUP2NjY1pD+/V3LJ3hODqcwV/930P4xp8Oa69940+H8Vf/9xBGh5cu0S/JBKvXsXHs0vcykUhofrTOTnsJNj4fQWsr84GcOnXK0LEs79xz2223QVVVHDx40GpTXGyGqqrapj1Ajd+QVgO3b8eOHZibm7PEBlVVNeEz4u0EJealDlIiIuJdA4CJr3bOFOC/UTjYCUEwptSKLAUQ9LHIwn/7t38zZAy9uHbtmlYyKNpwhyljEkK0sX7yk5/URI+JiYkJrQxYuHmjbuelgoxgA8tQ+9GPfqTbeY2ir69Pc/z6m9eV9RlVzSCVmEQqkWt4zf+uquX3eSKEwJ/tZWPn6Mvp6Wktu86/+m7Dgg3khnaIgSimp6ZsX17w1KlTzGFICDyrjM/iAwCqeKG0MefP448/bsqYevHee+8hk8mABkOgPn9V5+KCT60Iwj09PaxPFaUQ24xtdL0Q1OOB0MDWdXasAKCqqibo0Ubjs5BoA+vp09fXV3OZJVo5tFXtuszFPEunlgSb119/Hel0GkJrHEJk6Z5zaiaD9Ngk0mNT2mvpsSmkxyahVtCbUYgGITTHkE6nLS1NOTzMHFjU5v1rONxObrcV8Mzd6EMrQQwq91UKJe5HYF0jVFW1dUDX9u3bAQDBlWEoYY+hY8XvbAII64vY399v6FguLi724L333oOqqmhqFRCOmiNW8EweuwR4dXd3I51OIxwmiMXsF7i/JisiGR0UaLlgwx277gPIpZjz58+jv78fBBReuryGu2ahkBAEyJiamsKRI0cssWHv3r04ffo0CAREs+KJmUS8a0Ag4NSpU7ZR5ovp6urKOq0IYiFje2/EIsyp/+qrr+L69euGjlUNP/zhD5FOp+Hzt0LxmtfcORheDUH0YmBgAC+//LJp4y6XZ599FlNTU5A8YXjD+kZ9h7IC0M6dO22fkfX8889DVVUo4RaI3vL6JqRnp3Hj4PO4eeQV7bWbR17BjYPPIz27eMPrYnxZkWjv3r3o7e2t6LNm8fTTT2NkZASCNwhP82rDxiGEwL/mHgBM+JycnFziE9bxr//6rwAApW01BG91AkQleDqZMPzaa6/Z9nopBW+SLjZXL1iIzUyw2bt3r+1LlgJ5DeKbmkEk4/smLITQ3lFgj53o7+9nwgkhoDHj18dElkHCLKuUC0W1AnesilWWQ+OIeYJNrTQB52ss5Y7ySltlJqYx9v1XMf54TmQZf/wNjH3/VWQmKntm8zF5iVArGBkZAQBQn/n9sJaD4GeCDbfbbHp7e3Plvj5Q3n5STWcwNzyFuZHc9TE3Mo254Smo6fJFPgCIf5BVP9i0aZNlQZBLwXtCNdxtTAZ1PnJQQXg1m+ftGEDg4uKiP7x/zW13m/fcuu0uNtb+/fttsb45e/YsAGDlKsGWlZZWrWKCDbfTKCwXbLhjNxBYOuLHxVnwaFAviYASe6XBFUMIgY8yZ7dVUaw8kyPsWQmBmr8pEamCsIeVP/jhD39o+vjl8C//8i8AgJB/JWTJ2DnHq8Tg8zQjlUrZNsvmwoULWgRbtPFOU8cmVEC0gQkV3/nOd2ztbE4mk1qEfqh5o+6LBskThC/KShjaOctmdnZWi7r0t6y3xAbRE4ASYQ6zn/3sZ5bYsBizs7Pa/OdffZfhJeM8zSsh+EIYGxuzbS+bixcvYvPmzQAA77q7y/qMmskgPT2J9HRuXuB/ryTCW4o0QGpsQzqdtu08XIyqqppIIDVV72QWYnEQScbExIRWwsXOcEFAaLQ2s5qPb3Spg+XA/x1pLAIimrM+FhrjAMxpsKoX169fZ72bCIHQoY9jlbY0ALJUUK7Ezpw/f579mxEC+XbzSiVz5I2rAEJw9OhRXLx40fTxAWhZ3ETSt/IAXwvqvSbk97RVYsWmTZugqioC65ugNJa3V0qOzaD7r9/CuW/lSoOf+9bb6P7rt5Acq6znYPiuVoghDwYHB20pUExNTWnO1Kb7ll+ytBIa72Nl6bhQ5OLiUt9wfyIXUcxgRacIr49gbGzMFqWkeS+d9jbLJYuStLczu4zu+WPpt//e976HM2fOgBCCO+8011noYn/4YshH4xZbUh5csOF2m8nx48e1caO+taaPz4n6WPT7jh07bBeFefbsWa2/TjxiTukvPs4zzzyDGzdumDJmuaiqiq9//etIpVLwBzvgC5hXo5oTjt0GSQ7g5s2b+N73vmf6+OXyxhtvoL+/H1T0INBgzP0VbmHPwJdefhkDAwOGjFEtr776Km7dugVB9sHbYE4T2lIE29l99fQzz2B8fNwyO0rx/PPP4+bNm6CKD9424+diQij8a5gI8qMf/QiJRMLwMSvl+9//PlRVhdy8AmKovGyATGIaI+88j9Eduajs0R2vYOSd55FJVJiVtZ79Ps8++6zt5uFSXLlyBdeuXQMIhdhUvZOZEKpl2dgxW6QYTbCJW7v2E2JRgBAMDAzYbk4+dOgQAIA2mSdq0SZWeu3w4cOmjVktvKwHbY6DKPo4PQilENqbC85vZ3gpV2ldO6jfa/r4NOCF1NlWYIvZ8MxCQvUTVggheOSRR7BlyxY88sgjIIQgPVGZMLEg2RJkVmREplIpreRsudk1ekMEitjDbI3JeybaCV7+3Nvoh7+tvEzzamm8lz3D9+/fXxM9C11cXJZPX18frly5AkqB9RvNyzQXBIJ1d9injw0XQtpsKthwu3p6epCpIJiwUkz99plMBrdu3cLWrVvx27/92/hv/+2/ae994QtfMNMUF5uTSCTw3nvvAcgJIXaHC0tdXV2m1/j+1re+BQAIKSsgC+WXm1HVDJLpaSTTOQcY/3slvSU4suBHSOkosMku/OM//iMAIOhfAUWurvFsufg8TfAqDUgmk/j+979vypjlsm3bNuzevRuEUDS0PGiJDZQKaGh5CABzNl++fNkSOxYjk8nksteaN4Ia1CzdE2iEEmhCKi+bx06oqqpljgTaN5rSNH4hlGgbRF8E01NTljmASjEzM6Nl8bHsGnN+I29LJ6jHj8HBQdtlHV26dAkvvfQSAMC7vrzsGr0RY80QY01IJpNaaTY7w4MvhHgjiKjPRk2skT42U1NT2gaNxqwVbIgogYbYWsFuASh8fSy0Gl+Oh8PHOnLkiG3LFBXDBRWhQ9+AFJ6tY3fBZnZ2Vpt/lXutC+biY7/44ouW9CzUHCk6ZsKoqopHH30UnZ2dePTRR6GqKlJjlQUTLATP2LGiJM2OHTtw8+ZNCH4ZoTvNyR4pBRdsdu/ebbuy0jzrp/HeFtPK9Pia/Ai0BZFOp2uqf5aLi0vl8DXeijUiPD5zxYr1G+UCG6yEl7JuabVnpaXGRgpK2Vrr1q1bho1T9RUgCELZfyRJQktLCz75yU/i6aef1vrXPPTQQ/iDP/iDqr+MS/3w3nvvIZFIQIQCmdRGuTyRKFBIAKqqmrqY2rNnD/bt2wcCigb/7RV9NpVJoGd4Ky6PvKu9dnnkXfQMb0Uqs7xI7bj/dhAQ7Nmzxzab2YMHD2azawgaIuZl8xFC0BC9CwDw1FNPWVYOophUKoW/+7u/AwBE4hshK+VHiKlqBsm5SSTncqWK+N+XI/L5g+3wBVqRSqXw93//9xV/3mjefvttlglKJQSbjG2WHmll18qTTz5pu8bO27Ztw4ULF0AECf4Wc5rGLwQhRMuyefzxxy1xAJXihz/8IcvE8vjhazevZByhFIG19wEA/vmf/xmDg4Omjb0U//RP/4RMJgOpuQNS1JoSV4QQ+G67DwDLyrKb86cYLqpIzfo5y3gvnOPHj2NiYkK38+rN6dOnoaoqiNcH6jU/G6AYGmcBQ3YoDcEZHR3FmTNnAAC0pcm0cUkkDHgUJBIJ2wlYpUin05r4Ka7U1/HMz3fgwAFMT+vjpDeCzZs3Y3R0FDTkg7Ta/CxqjtTZChr0YWRkBFu2bDF9fI+HNYVXU/oJIIQQPPbYY+jp6cFjjz0GQgjEsE+Xc2eSLLPGa8EcyMskxx5aBSpaF9WsNAQQWNcIVVXx7LPPWmZHMfmCScNd5c2/mXQGM0PTmBnOzRUzw9OYGZpGpoL+PvG7mFDM+3K5uLjUJ1wsWbfR/BYH6+5gYx46dAjJZNL08Tmqqmr7tcZG+/WvAVhGUjzObDNyb1n1k5iLLqqqVvwHAD71qU9h8+bNEAR7Kmcu1sCbHfpogy2bTC2Ej7KSEdx+o1FVFd/+9rcBAGHvakiCPpuFapAFP8Le1QCAb3/729q9bhWZTAZf//rXAQCRYCcUOWLq+H5vCwK+NqRSKfz1X/+1qWMvxHPPPYeLFy+CCkrFvWtSyWlcPvcSrl54TXvt6oXXcPncS0glK3dcEELQ0PIAAFZ67NixYxWfwyhUVcU//dM/AQBCzbdDEBVDx/OG2yH74piZmcFjjz1m6FiVoKqqVrIu0HobqGh9415fUycE2YebN2/iueees9oc9Pf34wc/+AEAILj+QRBB31r5S+FtXQsxGMfU1JRthM9z587h1VdfBQD4s4KJVcgNrZDiLUglk/jud79rqS2LkUwmsW/fPgA5kUUPBH8ANMCic/n57QgXRoS4PTKrhWyWj5362Bw8eJCJWuEQqM88hy4hRMuysUOpjKU4deoUa9ouS6zvjI6QSAgk6EcymcSBAwd0PbeecOe7cs9aw/upLQahFPLdnQU2mYnPx/ZG6px+JcZUVcWmTZvw8Y9/XOv5IgT1uR+5ndxus7h+/bomBsQ/sNrUsUsR/yArybZp0yZLHYf5nDhxAiMjIxC9IsJry3tOzY4msPfP38F7/1/ON/De/7cDe//8HcyOlh8cyQWiHTt2WFIuz8XFxRx46dm1t5tXDo3T2iHA5yeYnp7WgoOsYHh4GIlEAoQA8bg9S6IBLMsGACtlbRC6fPtyHbKiKCIWi+GBBx7Al7/8ZezatQuvvvoqotHyapq7OAceFeevkf41HH9WsNm9e7cpQsW2bdtw4sQJECIg7rOmCXgp4r4NIBBw/PhxyyOBXn/9dZw6dQqUSmiIWlOSpyl2PwgoduzYYbmzbHZ2VisPF2+6G4JgvfNd8UQRjLDNvF2czQCLYuvu7gahIsItGw0fjxCCaPs9AIAnnnjCNv1Z9uzZg+PHj4NQAYF2c/o/LQWhAoIrWEbS97//fcs389///vcxMzMDKdIET/Nq08cnhCB0+/sAMEH26tWrpttQjNa7pmUlxLD1z3Lf7fcDAF544QXb9rI5cuQIJicnQRQPhKi+v5nY0g7AvICS5cAzN6wuh8ahUeaQO3nypOXBJxwryqFx+Jh2Fik4vF+T0NGiu1hBCIGwigmqdi1PdPHiRRw8eBAgBEpWLLESz71rAULw3nvvGd6gtxi/n5WKzszqu07ID1rV9bwWCTa8+klgfSOURnN6syxG6M42iEEFN2/exNtvv221OQByz8/YxiZQwVwnYmhNFKJPwtjYGE6cOGHq2C4uLuYwNDSES5cuAQBWrzdfsKGUaONa2bOQ79NCIQJRtG/wfjTKngM3b940bIyqnzSZTKbsP3NzcxgcHMShQ4fw3e9+Fx/+8If1+A4udcb09LQ2UXloxFpjKkQhIQAEo6Oj6O/vN3QsVVXxne98BwAQ9ayBSD2GjlcJIvUg4mWRUf/wD/9gmaNDVVUtUyEWug2iYM1vJEtBhINsw2x1f5ItW7aw+tSiF+GojUS+pnsAEOzduxcXLlyw2hwArK8OAAQbN0AQzbl2vOEOSN4IpqenbdOfRevL0rIBgmx9mSKOv3k9qORFX1+flslhBcPDw1qWT2DtfZZlhcqRJsjxNmQyGe3atYpbt27h9TfeAGBd75pipFgTxFgTUqkUnnrqKavNKQl3BonNbbpfR1JWsNm+fbttxIdijh49CgAQGvTNiFguQiwGUIrBwUGtlrbV2EGwOXz4sG1KUS4ELy0orjSmFJiY7Ytjl9K/xfAyUtLaNtCg9dn3NOiD1MlKyZmdFRsIsNLaembYGAkXloJB80ST2dlZbc0Z/6D1Ah8AUJEi9r7VAGCb/nxaBtKd5pWj5FCBIraxscAOFxeX+oKvg1vaBfgD1mSWrMkKNkeOHLFkfCAngESj9hVrgJx9AwMDho1h3/wiF8dy/vx5lloOGSKxPvq/EiihkAnb5CxTvgAA+jtJREFUGJ09e9bQsd5++210d3eDEhExG2XXcGK+daBExOnTp7UGjWZz+PBhdHV1gRABkZC1v1EszPp+vPvuu7h8+bJldvz85z8HAISj6yxtHF+MJAfgDzKHoh2cqd3d3dlsKIJwc2W9oaqBEIJwM8ti+clPfmJ55siZM2eYg5AQBDrM6/9UDkQQEWhnmU8//vGPLXNC/+xnP2M914JxyFHr+gQAgH81yzp67rnnLO2D9NRTTyGVTEKMNkKK2MP5DgDeNex6eeqpp2zpcH73XdZPjosreiI2NgOCgIGBAUvLHCzEwMAAE0UIsY1gQ0QRNMaybKyMNOTk96+xQrCplT42MzMzOfGvw5g5WWhnv/+FCxcMjaxcDslkEi+++CIA2CK7hqPcsxYAy3I0c23DBZtMwh5ltZaC28ntNoM33ngDw8PDkCJehO/Ut+dTNcQ/uAagBAcOHLD8uTU4OKiV7YzfYU1PvoasUOQKNi4u9cnp06cBACs7zc+u4axcKxXYYgV8DxsO21uuCEeYfUbuue39C7g4Er4gU4h5C1U9UQiLiOru7jZsDFVVtbJWEe8aCNR+wpZIFUQ8LMvmH//xHy1xpn7/+98HAIT8qyAKxvYfWQpZCsHvbYOqqvi3f/s3S2zQSmSAIByzn8gXjjFR64UXXrDcmfqv//qvAAB/bBVExdy5KBBfA0H04MaNG3jllVdMHbuYn/zkJwAAb3wlRMVvqS2l8LesB6ECTp8+jUOHDpk+/vj4uPYb+VffZXnPNTnaAjEYRyKRsKwPUn4GCxdI7ILcshLU48Pw8DDeyGYA2YXe3l6cP38eIARii379azhEECE2MUccF4bsBI/ko5EIiGSfNY3QyBxkdhBs9u/fz/rXRMIgJvav4eT3sbFrZgnAbEsmkyABH0jYmCwF4lVAG5mYZ7cygzt37sTQ0BCI3wOpU/+5ZLlInW0gPgWDg4Na6WszCIfDAIDMzJxpY1YDtzMSiZg25pNPPgmAZdcQk0t9LYYc8SF8F7uGecCZVfDnZnBFGErYmooN8TubAMIcqUZX8nBxcTEf7j9sX2VuL9R82leysa9cuYKpqSlLbEgkWH8v2Vr33ZLI2e2KkX4r+zyRXVyy8JqFIjFmM8odakY51qSs3Uamxu3cuTOXXeNdZ9g41RL1rQUhzJnK64mbRVdXV3ZxTRCP2MNpyO14/vkX0NfXZ/r43PnvC7ZBlKwvkVGML9AKUfRhYmLC0uix8+fPa87cSKv55ZwIFRBuYdks3/3udy1rLjoyMqJdM4E2e/SuKUaQPPA1sQhiLpyYyY9+9COMj49D9IfhaV5Z8efVTAapmUmkZya119Izk0jNTELNZCo+HyEEgbX3AmCZP4ODgxWfo1r279+PW7dugcgK5NZVpo+/GIRSeFYxYdjKMnql4M4gId4EatAORWrtAADLsl4X4/jx4wAAocGayOWF4Nk+3D4r4WW+hDbrMvmE9tYCW+zIm2++CQAQO1cYKqKLnR0F49kFvn6Sb1thK+c7ESjk29hz0kzRuLGRzSnpyfIbvFsJt7PBpEzDGzdu4NixYwABYg9Xvo4xmtj7VwMAtm7diswy1kV6wZ+bDfdaN//KQQXh1az3sx0DL1xcXKqDB65z0cQKgmGKUIRCVVXDKwYtxNwcC1yQbNy/BsjZx+01Avus4lxcsoyOjgIABKJ/KiAhBI888gi2bNmCRx55BIQQpFR9FVEKZjf/Hkbw/PPPAwDCnpW2zK7hiFRB2MMW/y+88IKpY3/3u98FAIT8KyFLlUdYqmoGc8lJJFO5yIJkagpzyUmo6vI2DD5PI3yeJqRSSS2DwyxUVdWc76HwalPHLhdCCIKR1QBgaWbJP//zP0NVVfiiKyH7opbYEGzaACp6cPXqVbz88suW2LB161bMzs5C8kchh+zlRM3H38pK1m3btg2Tk5NLHK0fIyMjWk8q1rum8iVVenYag7ufx9C+3PU+tO8VDO5+HunZ6WXZpTR0QAo1YGZmxvR5Bsg5L5WWVbo3+9YDuW01ABaFb+RzulI2b94MAJDaVhg2Bhdsjh8/brvoXN5EWYjHLbakECHOnKbnzp3TIv6sQhNs2q0rWcTHPnbsGCYmJiyzYyFmZ2e1BuXiOmOdz+I6JkjbaS5RVVXL+JHW2Ce7hiOtYdfPzp07Tcu854JNZmLGlPGqJTNhrmDD7xffqhikkH36FHICaxtBPSJu3brFhCULSCQSWlZY493ml6PMp+EeNr4dAy9cXFyWz8TEhNYvsc3CDBsgJxhZVYqSi/N6biONCNrn9hkZXFv2T/D7v//7+P3f//0l01FHR0dx9epVXL16tazz/umf/ilisRjiNtuguVjH2NgYAECA/oKNqqp49NFH0dnZiUcffRSqqiKp6rsB50KTUZu3iYkJbZEWUoxz7OhFOGvj1q1bTdvcHzlyBFu3bgXLrlleZkAyNY2ea6/h0vVc5OSl62+i59prSKaW50gFgHiEZU5s2rTJ1F42R44cQW9vLwgV4Q/Z97oJhlkZvW3btlniADlz5gxef/11AECk7R7Tx+dQQUK4hV27//zP/2xo5MZCbNmyBQDgbVhteamvxZD8UYjeEObm5kwtTfOP//iPmJqaghiMQWmyTyYJIQSBdfcBAJ544klT55lUKqVdN3KbfX6TfMRAGEIwilQqpTmqrGZoaEgr6Se3G+dkpl4fhDgr8cX/nexAOp3WegNQm+0HiM8H4vEgnU5bWs+7t7eX7a3yypJZAQ0GQEJBpNNp1t/MZuzbtw+Tk5MgPi9oq7GBBjQaAo1HkEqlsG3bNkPHKpeLFy+yDG6BQlphfmP0pZBWNgMCxbVr19DT02PKmO3trCdYasSa0i6VoKYzSI0xOzs6OkwZk+8pw3faT+ADACpShDayrBarntn79+9HIpGAEvEg0BGyxAZOw93st9i3bx+mp5e/H3VxcbEX586dAwBEYhT+gLUBb1wwskqwEUU2fjqtz/lKBe2PjlSfscntkyTjeg6VfSX8+Mc/xk9+8hPs379/0eP+/M//HGvWrEFnZ3lNDqenpzE6OmqbyCQX6xkfHwcAUKK/skwIwWOPPYaenh489thjIIRAIvrWoaVgdhslTmzZsgWzs7OQhQAUMWzIGHqiiBHIQgCzs7NZEcVYVFXF3/zN3wAAwoE1UGR7/UZ+bzP83lakUil885vfNG3cZ599FgAQCK0EpdZGbSyG7IlA9kSRTCYtybL5zne+AwDwR1dB8cVMHz+fUNPtEEQPent78dxzz5k69sTEhBbN7W2wX4mMfAgh8MaZjW+99ZYpY168eFELYAlueMh2gpYSb4ccb0MqlTR1ntm9ezdGRkZAZAVS3LqyIUuhZMUkq7LXinnrrbeQyWQgROOgfmN7Zkkd7F6xUxmnGzduMMcTpaAhez2zCSGgUfYsuHDhgmV27Nu3DwBAmxpAZOua0QK5kmxL7QmtgD8DxLUdpszLQueKgnGthpdDk1Y2gcj2W+sRWYSYFZLMCrBYs4YFAqUnZpCZTZoy5nJJjUwBGRUejwctLcY/QxOJBA4cOAAAmihiR0K3M9vM7H2UDy8/1nBXs+XrPX9rAJ6YF3Nzc9pzwcXFpfbh4kjrCuuf3W1ZG6wqiZYTbPTJxC0VtH9rsHrBJpW1zxaCTSWoqmpJg3GX+mBmhqWsUwi6n1tVVWzatAkf//jHsWnTJqiqCpHoWyueEmY3/x5689prrwEAQoo5m9FqIYQgqLAoMZ65YCTvvPMOjh49CkIENETN7z9SDk2xewEQvPXWW1oZGCOZmJjQfvtw1L49jwB2vXAbn3nmGVOfJceOHctG7xFE2u8zbdyFoIKIcBu7hr/73e8aNqeUYufOnUgmkxC9YUi+iGnjLhcuKu3YsQPJpPEOmW9+85tIp9NQGldAiVlXnmgxghseAp9nzGqYvmnTJgCA0rHWluXQOErHWgDMCV5uRriR8H46Usdqw8eSs2McOnTIkl5qpeD/BjQQsOV1QwNMRLPyWuHZLFZm13C4YGO3DJv8rDmh05xAA3EtE2x27dplWXPefLRyaJ32zJYAALmTPTPNEmzC4bBWySN5c9yUMZdL8iarMrFmzRpQE+bCQ4cOsdK3YQ+U5srLR5tFYAMT+U6fPm16bz5VVTUhtMHicmgA2ydxO9w+Ni4u9QMPCmrtsF6wae1g/swLFy5Y4tdXFOaf1WtLXypov7Gh+mdsKmufLBvXosJ+uyIXx8NrhBMDBBsA2qRj1OTD7Tai1vnw8DD27t0LAAgq7bqf3yhCCts47tmzByMjI4aO9YMf/AAAEA1tgCTarxYzAChyBKEAi/Dm9hrJli1bMDMzA0kOweOzby8STjCyGoRQnD17Ft3d3aaMqaoq/vqv/xoAEGjohOytPMpbVTNIzk4iNZvro5KanURydvl9j0KNGyDKAQwMDOCxxx5b1jmWw65duwAAnlhtzDNSoAFU8mBqagpHjx41dKzu7m7NKRhc/6ChY1WDFIjC287ET97Ty0gGBwe1skCelfYWhgVfAFIjey7xnnBW0dfXh4MHDwIA5BWrDR+Pen0QGpizhweAWM2VK1cAACSgj8NQ7zrVNMjs4naajaqqWjYLF0usRGhhztMzZ87YqkLCvn372BpTkSG0mVMOjMYjIOEg5ubmLC+xODMzo5VWtGP/Gg637cCBA6b1hXrggQcAALOXb5oy3nLh9nF7jYbvKQPrm2wdBCgFPfC0sXW52Zl9Z86cwfXr10Eliuht9ijZyQWbbdu2ab0eXFxcahsu2LS0G+MDrYTGVhGEsFYVZovkAODxsApIc3P6ZdgUB+1HotVLIbNZ+7i9RuA4webQoUP4q7/6K3z84x9HR0cHFEVBIBDAhg0b8Hu/93uWpdq65OC9Ggjsu3BcDG63ET0nDh48iHQ6DVkIQRaNLZuiJ7IYhCwEkUqlNMeUERw5cgRHjhwBAUUstMGwcfQgFmaN0rds2WK4E0jLyoqssfWGjCMICnxBJhSYkZUFsHImR44cAaEiou33L+scqblpXDvxAq6felV77fqpV3HtxAtIzS2vzjShAqIdzJ4f/OAHuHXr1rLOUwmqquYEm2htCDaEEHgizAm0c+dOQ8f6l3/5FwCAp3k1RL+9yjcV4199N0AIdu7ciZMnTxo61iuvvIJUKgUx2ggxGDV0LD3wrFwPgAk2VmaFv/HGGwAAoaEJ1Oc3ZUx5JSsRxDN7rGZ4eBgAQL3VB1mUqlOdqTI7kWTt4naazdWrVzEwMMBKxjWZ04h8MYjPCxIJQ1VVQ9d0lcLLqIrrV4EIS29x1UwGmfFJZCZymTGZiSlkxiehlukEJYRA3LCqYHyrOHbsGJLJJEjACxq17x6BxoIgfi+SySSOHz9uypgf/OAHAQCJngFTxlsuiR4m2HzoQx8yZTyeJRdYZ/9grsBaZqPZmX28nHdsYyMEm5QZjK6PQ/CIuHnzpimVGlxcXIzn/PnzAIAWG2TYyDJBvIkJR7y3jplwAWR2Vr9zGhG0P5e1zxVsdOKjH/0oHn74YfzFX/wFtm7diuvXr2Nubg5TU1M4f/48fvzjH+MjH/kIvvjFL1rS4NmF0djIFmQp6HiHmkhKZdFi/HvoCZ8wPTXQu6YYjxgBkHsYGcHPfvYzAEAosAqiTbNrOB45Ar+3FZlMBk8++aRh4+RnZQXC9mwCXopgeDUAJtgY7UxVVRX/8A//AAAIt9wBUfYZOl6l+GOrofgbMD09bUpG1oULFzAwMABCBShh68s/lIsSZYINv96NoLe3V+v94V9zj2Hj6IXoC8LTwpzz//Zv/2boWLznkdJaG/OM3LwCoAJu3LhhWvPrUnBBXV6xxrQxpfaVACHo7u7GxYsXTRt3IbQ5Xod4glJ1qtWpyaU/uBjZQAerhD0uttJ4DES0fiMPAEJWODJaCC6XRCKBLVu2AACkDavL+ow6OY3pn76MmZ/nMs1mfv4apn/6MtTJ8oMspA3s3t29ezeGhobKN1pntOyajkZbB+cQQiB1sD0St9loPvzhDwMAZq8OIjNjzz1+angSqcFxUErx/ve/3/DxJicn0dXVBSAnhtiZwDo255gt2PB5pfFe67MbOVQSEL+DZRFy+1xcXGqXoaEhDA4OghCgpd0e6zwr+9iEw8zXOTFp7zYr3D5urxE4SrDhtbrb2trwx3/8x3juuedw4MAB7Nu3D3//93+P9nYWSfzTn/4UX/rSlyy01Nl0dLB+J0nVvH4NepLMCjb8e+gJF2wUMaT7uY1GFllJEaNU+rGxMa3payS03pAx9CYSYmWDXnrpJcP6buzYsYP12vBEISu1c934g+0gRMC1a9cMj+zYuXMnLl68CEIlhJvvMHSs5UAIQaTtXgCsR8jExISh4/Gmh1IgBkKtT8suFyXEHA5nz55FOp02ZAyejSHHWiHVQBYJAPhXsmt669athpUvSiaTOWdhgz17+hRDBAFSlF0zVjVPv3z5MnOYEQKpozyhS81kkJ6aRCZPhMhMTSI9VX5WAFU8EJuZwGmHsmi5ki7VO5lL1akm/iqzDSwWbLhTlTbELBm/FLSBlQbitlnN7t27MTU1BRLwgbaa63ym0RBoYwzpdBrvvPOOqWPnwyPtxXb7O9/FDuZ8NyvDZu3atVi3bh2QzmDmrD16dxUzfaoXAMsGikQiho935swZpNNpSGEP5Ki9ApVK4V/N5pyenh5MTlYpwpfJuXPncObMGRBK0Hi3fQQbAGh6gK21Xn/9dbcsmotLjcNFkXiTAMVjj4CLtpXWZdg0NLA1wviYvec2bh+31wgcJdjcfvvteOaZZ3D16lV85zvfwW/91m/h4Ycfxgc+8AH8j//xP3Ds2DFs2MDKKD311FOGl1VxKU2tCzYpMLuNEGx46SxZsG+pg4VQsjYbVf7r1VdfxdzcHBQpDI9cG47UgLcVguDB8PCw1tBSb3jUuy9g33rmpaBUhNfPsjv4dzCKxx9/HAAQbFwHKhrXNK4avOE2SJ4wpqam8Oyzzxo6Fl+YSb7auI84gicAQgXMzs6it7dX9/NnMhm8+OKLAABvm717tOQjheIQA1Ekk0nDSvZ0dXUxZ6kkQwjVznUjNTAHjFWCzXPPPQcAEJvbQJXy0ukzM9OYePMFTGzNlTOb2PoqJt58AZmZ8rMCeFm0F1980XJnjyaw6pAVUKpOddWl1rJ2pVKpqu1bDvYUbJgtXV1dlpYU5PCyRWLnCkuyS8TOjgI7rIBnsAtNEctsKBehIQLA2Kz7Yj796U8DAKZPXjVtzEqY7mJ2fepTnzJlPO4g9LTWRtUG0a9ADLHnpFnXDQ9oiN/ZBClgr/1Bw13NEDwirl+/jiNHjlhtjouLSxXw/jWtHfYJlGzNlmYz8znN4QLIzIx+fWyMYGyc2RaPG9ffzFGCzWuvvYZHHnkEglD6RmhoaMC3v/1t7e98I+1iLp2dnQCAOXVqiSPtyWyG2c2/h55cv34dACAJ9o+EKkbM2sy/g56oqopnnnkGABAOdtq6FEQ+hFCEA6sBQLNfT1RV1cpD+QK1EfWejy/AnKlGCjbHjx/P9i4jCDXdbtg41UIIQah5IwDgscceM7RRL1+YSb6IYWMYASEUoo85HoxI396zZw+uX78OIkrwNK3U/fxG4m1nAtOzzz5riHP16NGjAAAp1lwz8y8ASHEmClvh7Egmk3j++ecBAPJq8wVAqX0liCTj2rVrhoviSzHLi1TrVO5L7zrVJLtvmNWzmHaZZDIZnDp1CoDNBJtYFCAEw8PD6O/vt9SWubk5bNu2DQAgdK6wxAY+7u7duw3Pgi3F1NQUrl27xmxpsL8Dntt47do1TE8vr8dfpXDBJtEzgPS4vYIC5/pHkBwYgyRJ+PjHP27KmNxB6Gmx//XC8bSwSgFmORB5j7nmh+3Xz1GQBTTdx/ZJZvX7dHFxMQZemrmpzR7l0ACgqZXZcunSJdMDc4LBIGSZieRjYzYWbEaZbUa0wuA4SrAph1/+5V/W/t8Odb2dyO23M6fpnDoFVbV3Glwp5lSWps2/h15MTk5ibGwMACBRe/dnKYVEmWAzMjKCqSl9xbjjx4+ju7u7QACpFSLBtQBY6TK+2daLa9eu4ebNmwCh8PjsXyKjGJ5hc+TIEcMWCt/5zncAAIF4JyRP0JAx9CLYsBaC7MPAwACeeuopw8bhoqrgtffvUQrRwzbzvASqnvBeU97WdSCCfRbU5eBtXQtQAd3d3Zq4oifcoSxGjIswMgIhzOwdGBgwvffE9u3bcevWLRDFA6lN/4zcpSCCCCmbZfP000+bPn4+MzPMeUoWCKiynOz9bqRQvhBXrlxhAoAggEYjpo+/EEQUQGMRALC86fW2bdswMjIC4vNAaLNmrUNjYdBYGMlkEq+++urSH9AZLpoRRQL1KqaPXynU7wGRJaiqihs3bpgy5urVq/HQQw8Bqoqp45dNGbNcpo5eAgD8yq/8iinl0ABoJVKlsHHNkvVGymbYGFXeNZ+rV6/i8uXLIJSg4c4mw8dbDg33MMGGBZ65uLjUKpcusWdAU6t91sENLQIIAcbHxzE8PGzq2IQQTQQZHbWvP5jb5pZEM5H86LmFMnFcjKW9vR1+vx+AijnVnKgrvUips0hjDoQQrF+vbx8V7kwiEECppOu5zUCgEgjYPaX3pM+dTUH/SgiC/Teq+chSED5PM1RV1b3UFXeiKJ4IaA31IuEonigIoZiYmDCklN6RI0ewa9cugBBE2uzfQJ5QQbPzX//1XzUnp97w+1OQa08YFmS2mdfb+d7X16dFcPtW3Kbruc2ASgq8Lcw5/8QTT+h+fk2wCdeWYENFCYKfiXz8O5gF/3eQV621rFeU0snKAL/zzjsYGBiwxAYAuR5u1J7bEiIwu4zqNbcY/DlO41EQm/0+vI+N1YINz1AWN6617DcihEC8Y22BPWbCs3qIx15lmxaDeNhexsyMpM9+9rMAgKljl00bcynUdEYr08btMwPeB4YqtROAImSvGTN62PDM09CaKESvPffd0Q0NIJTg0qVLhgQqubi4mANPFGhutc98LMsE0Qa2prIikYGLIKOj9sywSSRUcOnAFWxMZMeOHdr/b9y40UJLnAshBHfcwZokz6rjFltTGbMZtulYs2YNvNXWTC9Cc6LS2tmMFcNt11OwSSQSePPNNwEAkYD+ZejMgGfZvPzyy7r2EuB17z3e2nKicgihUDysBIwRDqEf/OAHAIBAfK3ts2s4wfg6iHIAQ0NDWj8VPVFVVbs/qVQ7UZccbrPegg2/N+VoC0R/7ZQPycfXwYSmt956S9csx0QioaXyi2H7lGwqF54VZKZgc+nSJc0ZJK+1TgAUwlEIDU1IpVKWZtlIUtYZZtPGyWqa2aXZaSInT54EkBNH7ARtZDZxG62gv79fiy6XsoKJVUi3rQEoxalTp9Dd3W3q2OPjbL9ElNrZI3BbefUAM/jkJz8JQRCQGppAasweQYFz/SPIzMwhEongF37hF0wbl18zgmJPMaIU1MOcmWZcM/wZHdto3woFkk9CaFUEgJtl4+JSq0xOTmqZps3t9gqwbc6WaLNCsLF7hs3ICLPL5/MhEDCuv3jFEt6BAwfwV3/1V4u+z1nsuFLHW00mk8E3vvEN7e+PPPLIosfPzs4WZOTwhY9L9dxzzz04ePAgEplxhITaaZaeUNkC8t5779X93CMjIwBqX7BJZWa076IH77zzDqampiAKPng99l1UL0bA1wZKRFy7dg1HjhxhJRt0gNenlj210wS8GNkTRWJmUPeFwqVLl/DOO+8AAMItd+p6biMhlCLUshHDVw/iRz/6ET7/+c+D6hhRPD09rUWRU7H25hoqsgw7vZ/HXBT2ZLNUahExFIfgDWJ2ZgLbt2/XrbHx5cuXkclkQCQF1FN7/dWEQARArn60GXBxRGztgOA3bpFfDsra2zA9eBPPPPMM/ut//a+WZJfbXbBBJg3AGsGG92qwU/8aDo0zm6xoSMvZvHkzVFUFbWsEDVsbeEG8Hgir25Hu6cUbb7xhauAfD7Qg3tp5bpNs6TYzS60EAgFs3LgRXV1dmLs6CPFu6/vRzV0dBAA88MADps6/g4NsXDFYO5UJxIAxQTnFpNNp7N+/HwAQv924yGk9iG1sxNilEezbt29J35WLi4v94PuPYJjC57dXPkVzm4Du49YINk1NrBSlXTNsRkeM718DLEOwOXjwIA4ePLjoMbzh7Ne+9rXlWWUR//AP/6AJSL/5m7+JBx98cNHj/+Zv/qbmvmOtwAUPLoDUComMcYKNFglFamczVoxA9C9/sHnzZgBAKLCqpppd50OpiKB/BcYmL+HNN9/UTbC5epWVWJDl2sgeKYUsM2cm/y568fjjj0NVVfgiHZC9tZUxEWxYh9Hrx3H58mW88847+NVf/VXdzp3fK4gQey0ayyI7B+jZ8+jKlSs4ffo0QAg8TdY7d5YLIQSe5tWYunwSb7zxhm6CDd9oCIGQLuczG263WYKNqqqaACivXmfKmIshta8EkWTcvHkTR44cwcMPP2y6DR4Pc8KpqZTpY5cDt4vbaSb82UdD9nuOc5sGBwcxPT0Nn898wZbfS+LaVaaPXQpx3Uqke3rx5ptv4k/+5E9MW5fy6FwarB3RnAZZJQKzyzE+9NBD6OrqQuLKLfhsINjMXrkFALqt/cuFCzZSsHayqaUAE5eMFmy6u7sxOjoKwSMimM1gsSvR2xtw6Y1z2LNnDzKZjK5BXC4uLsbDg15abJZdAwCtHUwusCIwhws2PJPFboxkM3+4nUZR8Yyuqqruf+zAjh078L/+1/8CwH70f/mXf1nyM//v//v/YmxsTPvT29trtJmO4f777wcAzKoTSKlzFltTHhk1jZmswHTffffpfn4u2FBSO6nrxXDb9Yp+z2QyWgRUwFc7mVil8Gft37dvny7ny2Qy2pwk1bBgw23XU7AZGRnBCy+8AAAINd+h23nNggoSgo2sR9bjjz+u67mNfiZz55XRTiw9vwfPxJKjLaBy7Tg2SuFpZk7NXbt2FWQIVwNvlMl7wdQaXLC5dOmSKWvSkydPslrzggipxfrnFqECxNYOAKxcnhW0tbHfIWNCX4LloGZLCHI7zSKZTGp9CUjQ2kysUhBFBrJlrazYA3GREQDEtStMH78U4up2QBBw+fJlnDt3zrRxuehBA7XTe47barZg86EPfQgAkDjXZ7kfIpNMIdHDvj+3ywxmZma00qhioIYybLLZQFxsMoq9e/cCAKLr46CCvQWQ8OooBEXA8PCwqXOOi4uLPvD7tnWFffrXcLhNVswtLS0tAICRYXvoBcUMZ+1qbW01dJyyr4qPfvSjNRu9vhSnTp3CZz7zGaRSKXg8Hjz77LNlKWWKokBRameRU0u0trbirrvuQldXFyYzNxEROqw2aUmmMoNQkUZ7ezvuvFP/Eku8waJA7TeZlwul+mbYnDt3DiMjIyBEhFexX7mQSvB52Jxz7tw5DA0NIR6vrl59MpnE3BwTO6lQu1lZvMSVnllZTz/9NBKJBGRfDJ5gs27nNZNQ8+0YGziNAwcO4NSpU4bMOXpDCMEjjzyCRx99FI899hg2bdqE9Nw0RI/+jkg9nTDbt28HACgN9n8OLYUYjIHKXkxPT+PgwYO61MvXIrt99nMolwP1MrvHx8eRSCR07z9XDM8KlVrbQQR7PM/ljpVIXu3B5s2b8ZWvfMX0CN2VK1mUuzppXvPxSshknz/cTrPo6+tDOp0GBAHEp891SQiBqqraf6uFBgPIzA7j6tWruO02c/sx7dy5k9nQFAcN2COzhMgShI5mpK/0YceOHab9JjzjgPr1DSrQ+3rJh9tqZkk0gAkjPp8P0+PTSPaPQG6zbv8w23MTajKN1tZWU9dxWgk9gWp9YfTCyGtG9JtTRu+9994DAERvs3c5NACgIkV4bQzDp2/hvffew+233261SS4uLhXAs1d4Noud4Fk/t27dwsjICKJR88rsc8FmaMieGTbcLm6nUZR9VXCHRb1x6dIlfPzjH8fIyAgEQcDTTz+Nj370o1ab5QLg05/+NLq6ujCRvlETgs1EhjmtPvWpTxkibs7MzAAACDFmMjdygc2hYJM+/y7Vwssz+jwNIMR+aaSVIAoKFCmM2eQYDh48iE9+8pNVnS+T1wugJktbZSHQt8SVqqp49tlnAQCh5o01G4ggyn74o6sxNXwJmzZt0q08pyjm5hdVzYBAv/tKVVU8+uij6OzsxKOPPopnnnkGqcQUFD0TM1R23ed/j2qYmprS5hmloV2Xc1oJIQRKQwdm+s5j+/btugg2XEylkjHCsNHPJiJKAAgAFRMTE4YKNplMBq+99hoAQOpYbdg4lSI2twOihBs3buDw4cOml0VbtYplfmUmJ6FmMiA2K+lilWBz6xYrlUT8Xl2eVaVEc3V6Bqgie4f4/cDgsGarmezYsQMAIKyyPlMtH2FVmybY/Jf/8l9MGZP3huR9YfSg1PWSmZyBENZHnLeihw3ASht+5CMfwVtvvYXprl5LBZvpUywz7Vd+5VdMXY/y31wMyLqOW+qaSY4nIMf8upyfZwONjY0hmUwa0lcsmUzi0KFDAIDohuqC58wiuj6O4dO3sH//fnzxi1+02hwXF5cK4FVEGlvs58tSPBThKMXYCKvcYqZg09HB/L8DAxlkMiootZfPZuAG8zm0txvrH7DXjshk+vr68Cu/8ivo6+sDIQQ/+tGP8B/+w3+w2iyXLFz4SKijSKrTVpuzKGl1DtMZFt3267/+64aMwUUOqqMDlcMX2Fu2bMEjjzwCQghSmYTu49CsqDI9rc+/57FjxwAAXsX+EVDl4PWw73H8+PGqz5UxoXmzKeWtsudOp9O6nO7ChQu4evUqCKHwR62vXV4NgfgaAMC2bdt0c2TLcs7prmb0+c05hBA89thj6OnpwWOPPQZCCESPPpt4jpq97vO/RzWcPHkSyWQS1OOH6K+tXkcLIcdZ6jYvJVQtvMQlMUCwKfVsyiT0XQ8QQkAkfct1LsTRo0dZiStRgtRqHwGQCAKkdjYfvvrqq6aP39rayjLWMxmoNiyLlhlj5W7Xrl1r6rhaFLxOvXOKRXNVVasuQ0c81jjd5+bmsHv3bgCAaDPBhttz+PBhjI2Z04tzdHQUgL6CTcnrZXxKt/NzW7nYZCa/8Ru/AQCYOnYZakrftU65pKdnMX26t8Aes0gmkwBYho2elLpm5kb0e2bn22vUPuf06dOYmpqC6JMQaKuNUq/RDWz/ePDgQVP2fy4uLvqQTqdx/fp1AEC8SR8fn97+GW6X2aVvV6xYAUVRkEwCt27Zb167fp2tHdavX2/oOI4VbAYHB/Grv/qrWpPZf/qnf8Lv/u7vWmyVSz6tra348Ic/DAAYTV+z2JrFGUv3QUUGd9xxh2GpyDzqN60mdT93qQV2Mq2/SMZt16sx7dGjRwEAXqU2IqCWwpP9Hvx7VQMvhwYYk2FTUuRL6pM5VTgOsz3/+1QD70fiCbWCCrXbDwoAPKEWECqiv78fp0+f1uWcgiDkyiHpvOlTVRWbNm3Cxz/+cRbZraoQZH3L2Kiq/oINAEih+phjgNx36e7u1uW+4uU6WaaKvpR6NqVn9HMYcrjtkwaLBS+//DIAQGpfYZtyaBx5BROA33zzTd3m23IRBAGdnZ0AgMy4OQ7uclGTSajT7JqzTrDRxwlfSjSngeqyJawSbPbt24eJiQkQnwe02V7zMw0HQWNhpFIpbNu2zZQxtRLdOooPJa+XkI5BFllbjS5DWYpf/uVfRlNTEzLTs5g502f6+AAwfeIKkMpg48aNuOeee0wdm18vmZS+67xS14wc1W+dl8m7vvVa5xVz+PBhAECkMwZis4juhQiuCINKFKOjo5pvy8XFxf4MDAywwEABiMSq99eU8s+Mj1a3Log3WiPY5O8NenvtJdgkEipu3WLBsq5gYwBjY2P4xCc+oTm4vvGNb+CP/uiPLLbKpRQ8rXc83YeMmrLYmtKoagZjaTaBffGLXzQs22DNGuZMSab1dyiVWmBLgv71wOfSzOnBv0s1DAwMZFNICTwee23Wl4svm2Fz4sSJqhuC50roUUMEm5IiX9KIa5MtEvRqkM4FG1/E/mUWl4JSEd4Qy5bQ0ykUDrNMkrQBAhzPBDKq7GJmjtnMv0O1nDhxAgAgheojiw8ABG8QRJSRTCZx9uzZ6s8nZCPCjChXVuLZJHj1zcoCoNmuVym9UszNzeGNN94AAMgrOg0bZ7mITS0gigcjIyPYtWuX6eOvW7cOAJA2KSOhXDITLOsqFouZWgoCMCbDplg0r7Y3DhdszM6S2LJlCwBA6Fxhy9KmwlqWscbtNBp+baoz+qyVgNLXCw3oJ65ksrZGIhHdzlkuoijic5/7HABg6vhl08dn414BAM2pZiZcsFGT+mYXlbpmpJB+fZW4vbKsbym3fLhgE15r7nxfDVSkCK2KAMjZ7+LiYn/6+/sBANEY1aXkVyn/zHCV2SnRBuZH6uszP7iBB8JfuWxNJuxCXLnC7GlubjZ8b+A4wWZ6ehqf+tSntFIgf/Znf4Y//dM/tdgql4X4xV/8RaxcuRIZpDCe7RFjNyYzt5DCLKLRKD796U8bNs7q1asBAHMGCDalFtgi1bdxKZATm/h3qYYDBw4AABQ5AoEaE2VlNpIYhCB4MDc3p5V7Wy48Qtqo3j4lRT5J/6bjlDL7E4nqS/QNDg5qv6svsqLq89kBLjxxIUoP+P2ZmrFnA/DFSM0w56oec4yqqtpaQQo3Vn0+u0AI0b6PHmXReJSrakAZjpIOQ4/+wQTcdiPq4XN27tyJ0dFREI8XYrOxDSqXA6EU8koWTMEzgcxkxQo2J6tT+mdQVUNmktnD7TMTrd+fpJ+QqLtonr1n9Cp1Ww7pdBpvv/02AEDstOezXOxkz+adO3ea8ttw0SOjo2ADGBtkoSbYOtUKwQaAtmdL9AwgM1NeVqEQ8qL1jz+F5v/rE9przf/XJ9D6x5+CECpfzEoNTyLZz/rn/tqv/VplhutAQwMLQknPJJExQLTJ/6+eJCfY9c3tNwKeWR1eo78TzshS0uFO1ouJ2+/i4mJ/uAgSbdCvHFqxfybWWJ3LPxpntnFxyUzuuusuAMClS/YSbLg9d955p+FjOUqwmZubw2c+8xns2bMHAPDHf/zH+PrXv26xVS6LQSnVStWNpq8aFpVdDaPZ7JrPf/7zuZIEBsBLcaQyCaQzxpRFy/+v3qQzSa0vjh5lRXhDSJ+nvhypPk8TgNz3Wy687FwmkzJmo11K5JP0L2uRSbPMOr+/+qj67du3Q1VVyL44RJ1LcVkFF2y6urpw44Y+onZOsDG2n4cRpBJMZNJDsLl27RoGBgYAQiGH6yfDBgDkqD7zDJBXlkTnnkcco59NADTbjSqxAkBzMEsdqw3JetQDKVsWbceOHaaXRWttZdmCGRMd/+XAy6Fx+8xEy5S1Wfm8fEg2K02PoIpyOXbsGIaGhgBZgtDebNq4lUAboiBBP2ZnZ7V9p5HwrFJ1Vv/9gVFwwSYUsqZPyPr161lmXzqDmbPlRQ4TgUKM+iFGcmtSMeKHGPVX1A9m+hTbO77//e9HPG5+lYBwOAxPNnMvOaZ/NrVRJEeZrS0txgQ9JBIJzYHqbw3qeu5SpYpmx/SbN/2tLGju0qVLup3TxcXFWLQMm7g+gk0p/0woUt25eYaNFYINF0R6etK28gP39LB9IxeUjMSeO0aD+MIXvqClpn/sYx/Do48+iq6urgX/nDt3zmKLXQDgt37rt+D3+5FUpzGtDlltTgGJzDgS6ihEUcLv/M7vGDpWOBxGUxNzss2lay/yndvc3NyMYLD6RfCZM2cAAB4lVvW5lsLIiKhi+Pfh32+55CIWVWTSxjjezHCkptNsM6NHBCZ3TnvD5jvejEKQvJB97JrRqwwCL1mYmrFXaaKlUDNpLStID8FG618TjJniMDVznpHC7FmiRyQmn88zSX0ju81CzWSgppiTM1BlP48Fx1BVrcyY1NpuyBh6IETjIIoHU1NTppdV4YIIF0jsAheQrBBstFKgojGZsrqQtU3LBjKBrVu3sqFXt+veNF0vCCFa9o8ZZdG46MFFkFqA26pXCdPl8MlPfhIAMHPmuqnj8vH4+P8/e/cdHdd13Yv/e6egg6gECBLsFRRFiVXsRewSRUqimtWoajVLsiTXxLGdvPWWlx07zcmLncTPjv/I8rOdajs/x3kvduw4stWoQrGBBSRA9DK93XJ+fxycixlgAMxgbpuZ/VmLawGDwb0HlzN37j377L2tJkmSfk4TQZB8IIJLZgVseKltwFPugbfK2AUc6UoVxYaNO/YVTfz6paOjw7BtEkLMNTg4CACorjHuWsbo+ZlZo2MTY7XSDTfcAI/HA5+PYWjIOQGbi+08YHPTTTeZvi9nXuWa5B/+4R/0r//jP/4Da9euxY033jjpv4MHD9o4WiJUV1frdYZ9yjWbR5PKp/Lx3H77bWhuNn+Vn8hMSSj5F7CJj45Z1KnPBWNMD6iWltTmvL2ppFsRpSjm3dyUevmNa64B45KSEj0rRVXzcyIVANTRYJMR9UFFObSyqsLJygKA0tG/57333jNke6JerByytidBruSIDwBDbW2tITfzogG9q8T48pDjpTvPqHHzMg3co3+T+BtzIRYSaLH8mfRJpsX5uD0ej2krnc+fP8+ztdweeBqdmREAjE4yz5kLAJb3sRHXN5rfD6Y4p2ehNswXChmRGZwtPWvF7eCAzWgw28qAzS9+8Qu+68XO7kUnxvfLX/4SmgklI5OZVRLNTGKsdgZs9u/fDwCIXeozvJ/LZNRQDIlu3p/q1ltvtWSf6YjrpPzKsDE3gH7p0iUAPPhh9OKZdKWKyuqNq0xQ0cTv+fr6+hAM5t88ASHFaGiIX2MaGbAxWtUsPrbh4WHTr2XGKy8v1+clLrY7494gENDQ28uPw80332z6/pz7yiAkycmTJwEAETYMhVlXdmEqGlMQ0voBAI899pgl+xTBjrgJfWzMJjJsli9fnvO2/H6/fjFa4jU2ZX28dCuiZMW8FcDi7xGrvHKhr3w3oYSeVUR2UK4r30OhkH4jVlpZWOWtxN9jVMCmra0NACBHfWAmlbkygxziEyBtbW2G3GiL1e2SBZOl6c4zatTETIPRv0lfwZ8DsVhBizmrlFWmxLhnz54Nl8ucy+I333wTAOBpnG3J6ykX3iY+ESbGbJXW1lb+WtI0qEPOyKZmqgp1dEXhxo0bLd+/LPPPbie/ZkSGi2JRkK23txcXL14EAHjmO68XVDJ3SyPg9WBoaAjnz583dV96ScFA/pyHNT8f69y5c20bww033IDm5mYwWUGso9+SfUbbewDG921WpkgmxL4TeZRhkzA5w0a8TyvnGn9vma5UUWmNcQuCvJUl+vba29sN2y4hxDx+P69mUVFpfnWFmaqs4td5mqYZstAvW+vWrQMAtLc7Y07i4kU+jiVLlliy4KSoAjaMsaz+UUqpc8yfPx8bNmwAAD1IYrewNggGDYsXL7akfiEwFuzIy5JoBmbYiEkMAHBJ5k5kpFsR5fXk3k9lMi4XX61qxOSHyLDR1DwO2IwGm3Ito9fe3g7GGNzecrhN6LVjp9LRkmgXLlwwJP15zpw5PKOJsdGslfwgh3lGkFiJkyu9h4fJ5xgg/XnGXW7eeUb0UDGiT8lYhk3+TBQmE+MWf4cZTp06BQDwNJi3D6O4G3jG3unTpw0J6GVKkiT9Ok/t77Nsv1PRRoYBVUVdXZ0tGTb6tY5JgURDuKwN2Lz++ut8t00NkMrM6xtpBMnthnsuf8+b3cdGBD20gLNKCk5FC/KxzptnX5lISZKwc+dOAEC8Y8CSfcav8PvYXbt2WbK/yeRlhs3oWM2qaiECNlXzzOmrZHYp6ap5/F7J7AAxIcQY4jrP43VuwCa5KnjyHJxVRBZL+0WHBGxGA0cikGQ2B98BEJJK1PkNqc4I2IjA0eHDhy3pOQCM9WWQ1fy5IRMSGh/zwoULc96WvurUglNYuhVRHo95E/4SJH2/qprbB5OoaV4IJdFyzbARJeZKymtzHVJGLO1HUjYLkCQEg0H09vbmvD1JkrBkyRIAgBIN5Lw9q8ijY7VjYjVX6c4z7tIKE/do3OtSX9ltZkaQibSo+T1KRMBGBEOczFVZDam0DLIs48MPP7R035s2bQIAqH25n8eMoIyeTzdu3GjZdV4y/cbYoX1aAOgBG6tu4n/7298CANytzi0tmMzdyifF33jjDVP3o/eACsfALC5ZMhNM08Ai/NrUzGB5JsR5J37Nmvr8Yj9iv3bJy4DNaDaQWZ/XV65cAQBUtphbvcEsYtyXL1+2eSSEkEyIhXMWtEqdMUmS9Mq8dgZsOq6okGX7+9i0X+QLlKwohwZQwIbkkUOHDgEAoswHhWW3KtiDUiwq2Y4F3i36Ywu8W7CoZDs8yH6FnsZUhLXBlHFZobGRlz5StPybgFdHxzx7du4TVh4P/1Rj0ExteC+YvSIqdV/8RluSpJwniObP5w1v5UT+ldAT5ATPzFqwYEFO2xHl0LwWBGzS9j1KmJd9ILnc8Jby4Jz4O3MlgsP5FLARYxVjz5XVE7RWnmcEI/5GPWATi+jnr3yixXigyawJoP7+fnR2dgIAPPXOL8coSRLco+N85513LN339u3bAQDqwIAj+tiovT0AxsZlNf1cYOE5wenefvttANAzV5zOPZdf877zzjum1n5PzkJmCfvfO9Nh8bFJH7G4yC4isy/RPQzN5GOn+CNQfWG4XC7LVudOJt8CNkxjkAO8LLpZGTZ9fTy7s6zO/N6FZiit4wsK+/udsbiVEDI1sTjXyYnUwNj4cl1MPBMLFixAfX09FAW4fNneLBtFYZRhQ8hk5s6dq5e6iWrDWf2uJLnglcrhlcYyI8T3ojRMNmLMDwYNzc3NuOGGG7L+/ZkSARuNyXk1MaYxFRrjN0Hib8hF8s2d2G6hUJNKgOXaT0FkM4mgRz6S43zsuWZmXb9+HQDgKc0tUycT6fqRKAlzsw/E39XV1WXI9vItYMM0DWqMByYXL15syDbLy/nnBVPyL0A+HW30byory31SoqmpCW63G2AMWiw/Jn6SiV5BZgVsRHaNq6YOkrfElH0YzdPIJ8PF2K2yePFi/v+gabaXRWOKDHWAl0jasWOHLWMoLR1dUOTkjInRm3d9rCYaHh7WV8C75zg/+AkArsZ6wOOGz+czddV7SUmJfj5nsdxLXZpNjLGiogJer9fWsSxcuJCXZVM1xE3uYxO7yIPAa9euzbnUb67EAjolmB/XOGo0AWg8eG3E4r/xIpGI3h/VyN4yVhLjFoEnQki+cG5JNAD68KxcVKjvWpKwefNmAMCZD+2d97tyRUU8DtTW1mLFihWW7JMCNiSviFWOkSwDNkYT+9+2bZulq7Bra2v1GxtFi1m231yJsZaUlBjSnKu0tFQ/Dvlc7isdkYlkxIpDkZWS1xk2Mp9MFdlCM6UHbErMD9ik60fiKTGvHwkAffvd3d2GbK+1tRUAoMbzoy+JmogAYPB6vYaVVxGl1eSQz5DtOYkS4k0ujegp5na7x7JsIvl3rtEifIJGvOaNJjICPHlQDk0QY3377bctz/gS13mKAeUdc6H29wOahnnz5hlSynUmSkpGA3w2rGjMFBsdmz5WE509exYAINVUO75/jSC5XXA11gEYG79ZGhoaAABa2PmBcy3M7wvEmO0kSRJ2794NAIi195i6r1g7P6/t3bvX1P1kQhx7JRy3ZRIuW0qI3x/V1NSYEuSLRsfeN+5SB9cnmoK7lNctCofzs0QtIcXKhqq7WZFsDNgAwJYtvErSmTP2BmzOju5/8+bNOS+szhQFbEhe2bZtGwAgog3ZenEZ0YZSxmMVl8s1lsKuOf+GTFDUsZrDRgS4kntsxBP+nLfnJOLvMSJLQKxAU5X8Ce4lY0yDphpTSk80z3a5zG8gn7bvUYmZ/UgAycVvLmMxY/6vRSacmsiP84wm83E2NjYaFkRfvnw533YsDE1x/orlbCihEQAwbHWQCA6rkfzL5lPDPMiUa9nFybz11lsAxrJW8oG7rhFwuTA4OIirV69aum+RzSLKkdlFBIysXpiTTGStMMW5ARtYGLBpb28HALgaak3fl5HEeMX4zSKCzprP+YFzMUazAuXZ2rNnDwAgeqHHtPtLpqiIXeaZDyJAZKf6+noAAFM0qBHnX+OIcmhi3EZLXlAoh51/PNKRQ3zcZh0jQoixxKS/pjk7aC4Svd1u8+dR0hEBmwsXVCQS9h2rD04rKeOxAgVsSF7ZvHkzysrKoCCOOLNnYkhmMcRZEJIkYefOnZbvX6xkFkGQfCCCS0aWnLnxxhsBALGEvdlWRovF+d8j/r5c6JPuSv68VpKJQJPb7UZdXV1O2xL1rhXZmmNhdT8SVeaZMCKgmyuRpaKa2HvHSCKwZGTz4rq6Ov14KoHCOs8oQb7oQJQZzZUesAnnV8BGkxNgMg/m5prFl044HMaHH34IIL8CNpLbzYM2AN58801L971161YAgDYyYmuJPbv71wBjn1ss7NzzsBibFY3jL1y4AABw1eeeqW0lMV4xfrOMBWycv7pe8zsrYLN161aUlpZC9UegDJhTCjZ+dQAsoWD27NlYvXq1KfvIRnl5uf65F73u/MVv0S4fAOMWmozn8Xj0e41EnpSJGy8R5AEbJ2SuEUKmJ7IFNQevy2GMQR1NbLGrhOnSpUvR3NwMWeZBGzskEgwXzvN9W3lvQAEbklfKy8v1VUkhzZ6GeiGV73fjxo2G9GPJlpgYS6jOX0EnyCq/eTSyrIjoHSQCHIVCBKDWrFmT87b0SXc1Ds3JVwKTUEaDEI2NjTmnneoT7yb3krGLMhpYMaoRq3jtMFWGpsrTPNt+onSb0XXNb775ZgBAwj9g6HbtxBhDwj8IYOzvy5XoeaQGfYZszypivE1NTaisNL5s4enTp6GqKqTyCrgqzC/HaCS7+tg0NDSMZbcNDVm6b4EpCrQRnoUm6mbbQUymakHnXu9pAT42MwKe44n+Na4645vUiywqM7KpXHU8YCPGbxaRea4OOX/yXR3kYxRjtlt5ebm+YjZ6wZzsPrHdPXv2WFZKZTpr164FAEQ6R2weyfTEGMWYzSACNiJTJd/Io5lSuS5yI4RYQ291IDs3w0ZTAbH+1OOxp1xkcsnkDz7IvCxafb2EP/2zanzlD8fuwb7yh1X40z+rRn19dtd7588pkGU+12LltYszrhYIycKhQ4cA2BiwGd2vGIfVRKmshJo/E88iuGRUM3AAaGtrAwDEEz7Dtmk3xjQkZL6yz4iV73V1dfokpCI7d8JnMqL3jhETQXPnzgUAKPH8Ow6ZEH/XvHnzDNledXW13hBXjTn/XKPE+RiN+vsFEdCQCyhgo0YCYHIcJSUlhmXY3HTTTQAAZWQgL2rhC/II/38V4zfa6dOnAQCeOnMWd5g5yeyu4yt0RYaQlcSNkBa0J2NLC/HzaVVVlSnNrTMlPvtYwLmZa2z0/8jSgE2tsQEbSZJw33334Wc/+xnuu+8+SJJkaB8YVy3/LL127RoUxbz66+J8rgz4TNuHUcQYjfoMMoLoKxM9b0wvwGSMMX27TuhfI4hs/miX8wM20es+AMZUIJiMuO5Vovb2SZgpJcIXWBnRB5UQYj7xXo2EnXvvlDw2cY60gwjYvP9e5gtJ3W4Js2e70Ng4FvZobHRh9mwX3O7s7p3ef59/LuzYscPSUskUsCF5Z+/evfB6vZBZBHHN2slXhcURYz4AwMGDBy3dtyCCHnKRZ9isXLkSAC8Np6r5uRJqvIQcBGMaKioqDCkTIUmSvh0R/MgncsK4iSCRmabEnTvxNVOapugl0Yzsw5FPQS41xscoxmyUDRs2AAASvv68CkRMJTHCa+ivXbvWsL4Ta9asgdvthhaPQos5t3zTeMpowMaoTKPxRLDDXWd8Pfm0k8xR4469GPOFCxf0HmBWEecxETixGgsF9XHY1b8GSArYhMJgooC4w4gMG7N6QAnBYBCDgzwzUARAjMIYw5NPPoklS5bgySefBGMMLGjcQgWpuhJwuyHLMrq6ugzb7ngi+KENB8Fk5044M1mBNsJfN04K2Nx6660AgETXINSQsb0f5X4/VF8YpaWlep8uJxD3UrFec8rAGUWNK0gM8/ekGLMZxOSpHHF+Znk6SpQCNoTkE1G+MOh35jUeAAQDfGx1dXW2ZdgAYz0ur17V4PdZf7xEwMbqlhgUsCF5p7q6Grt27QIAhLQ+S/cdUvn+1q1bZ/jEYKbEfkVfmHwgj/bbMXL1e3V1tT6ZUSh9bER5t5UrVxpWLkHcDEfCvYZsz0qRMH+/GXFzJoKFcszZN6UzocT45GJVVZWhZRBEsE+JOv+YKVFeXsXoevhr1qxBRUUFmByHEvIZum27iICNkQ0Ty8vL9XONPGTt5/JMMcYgD/OMWbMybM6cOQMAcNcaH7BJN8msRYybZHZVVEHylkCWZVy6dMmw7WZCnK81vz2lnVR/IGUcdmlubuZBVcbAQs7LdOSBDT7xbvax6ujoAABIFWWQSo0JNAuSJOFb3/oWLl++jG9961uQJIkHWQzcvggyXb161bDtjtfU1ITa2lqAMajDzl2cog4FAMZQV1dnawbbeHPnzuXliBkQvWBslk30HN/e9u3bUVFRYei2cyGur+ODIWiyc0snx/sCAOOTm2b2Z8n3kmiih01tba29AyGEZEQP2AScG7AJjY6tvt74e5lsNDQ06C0R3nvf2kUpI8MaOjs1SJKEbdu2WbpvCtiQvHT77bcDAIJan6WrnoOjASKxfzuIgI2qxaEx515cCxpToTK+OtfoIJdYFR2NDRq6XbtE47xe//r16w3b5v79+wEAYX9nXmUIqEoc0dGAjfgbcrFs2TK+XTkKVTZ25aTdElFeymLlypWGrgbfuHEjACA+Ynx5ECOpiSjkMD8GRr53AF5bWGwzMWxOXXsrMcaQGOF/h9G9OcRCikRPh6HbNYsy3A8Wj6Kqqgrr1q0zfPvxeFwv4WRGwCbdJLOrwuBJ5ho+eXX+/HnDtpsJUfJGHRq05XNLG+SZV2b2SsiEy+Uay7JxYB8bFo0BigKXy2X6IibxXpIMLocG8PPi97//fRw8eBDf//73wRiDq7Lc0H1IowEbM/vYSJKk939SB32m7SdXon+NGKuT7NmzBwAQ7zC2DGr86kDK9p1i9uzZfMKQjZUccyLRv8bsjCzRBzLuz8/7hLiPj1v07SSEOJtYtBCwIWMkU2Jsor+tnUR2ywcWB2xE35wbb7zR8sAVBWxIXtq3bx/Kysp4WTRmzSoymcUQY35IkoQjR45Yss906urq9FTnhOL8le/x0THW1NSgpqbG0G2LSbZovFACNvzvMHLSedeuXSgtLYUsh5CIOb9GtRAOdgFgWLlypd7QPBfV1dX6CmAR4CgUiQjPzFq9erWh2929ezcAIObvBVOdW14lNnIdAM+GMWO1rjgO8YFOw7dtNTkwCC0eRWVlpV7uzShiIUOi/zo02fmrU+PdfOL04MGDKC0tNXz7Fy9ehKqqkEpKIJUZO/kLTDLJXG7sym13LQ/YnDt3ztDtTmflypV8FbosQ/P7MvodqaIClcfvQsXRo/pjFUePovL4XZCyWNHOGIM6GrAxOgA8E3p5OAf2sRG9dVpaWgwrrzgZkZniqjGnfroIDJoVIBTjNjPDBkBSwMae7LRMODlgI4K0iR7jrhMZY5C7R1K27xSSJOnXAuErQzaPZnLhDj42sZDILGJCUgQ+8gljTA80OWFilRAyPfFeDYw4N2DjH+ELxJ1wXhFl0T74QIGmWbegy65yaAAFbEieqqys1Js2WlUWLaTyklKbN2/WV+DYIfniOiI79+JaiMg8CLFp0ybDa8HfcsstfB+xAWia87ONpqIoUcQTPkiSZOgNSWVlpT7hHPCbt7LTaAEfH6uRwVGRRhsLFU4DeWDs7zE6YLN8+XK0tLQAmoqY37kl9WLDvCeAeJ0b7cCBAwB4KTEtkX838cni/dcA8GNldJBi5cqVWLp0KaBpSPQ6O7jFmIZ4D584NStjtrubZ6a5KqtN64Ni9iSzu5JPMou/xSoej0cvU6cOZHa+llwuuKqq4Kqs0h9zVVbBVVUFKYsSoywUAovF4PV6eXkkm42Vh3PeAh0xJiMWVUxH9K+RDM58sYoYt/g7zKJnEw867/UiODlgI97zymAAWtyYPibqSBhaLAGv14sVK1YYsk0jicC0CIo4kQgmGb3QZDwRII/0Oi9APp24PwY1xjMejSxBTggxj5hT9A07dx7LN8yDSXbOfwrr169HRUUFAgGGjg5rglyaxvSAjR096ChgQ/LW0dFVlEHVmrJoohza0aTVm3bRAxWy8yeeowl+cyrGbKQVK1agubkZjKmIxp1/LKYSjvIJ8TVr1hhen/muu+4CAAR9HWDMuSs4BFkOIzrac+fOO+80bLuiZ0cskP+lrQRNTSAe4u+xrVu3GrptSZKwb98+AEB0sMPQbRtFUxXEhnmGjWgYbLTW1la0tbUBYIiNBjzyEWMMsT4epDh48KDh25ckCYcOHQIAyAMOL6MXGAGLx1BZWWlaLWJ9gtmE7BqriLGbPcmcjpiYU/v7Ld2v2N+NN95oSuZVtsSktjbivIwJMSYrJt6HhviErVReZvq+zCDGLf4Os+gZNkPOe70IYmxODNg0NTXxVcQMkPuNOYaJXh8Afs9idibaTOjlpbt8to5jMkooDtnHe6GKcplmEQufwr0hqAnnTqCmE+rkQdply5ahrCw/z5OEFBsRXA2HGGIRZ87RDPXzc6HRfWJnoqSkBNu3bwcAnHrHmEUV02lvVxEKMdTU1JhSQns6FLAheWvPnj2orKyEghhizGfqvhJaGHEWhMfj0Sek7CR6BUQSg1A1a05WM6FqCYRHg0pmRKQlSdJTE4NhZ6/onk5gdPxmpFru3r0bdXV1UJWo3hfGyUK+DgA8m83IiwPxAR8L9UNTnfu+yUY00AuAYeHChaZcSN122218P4PXwByYxRYb7gLTFLS2tppaauTYsWMAgGiPtc3XjST7B6BGg6ioqDAtuLVp0ya+rxFrJ9mzJQ/z8a1fvx5er9eUfYgghyuPAzYuGwM2ItNUHbA4YDO6P7NL72RKTGqzEZ+9A0lDjElkdZjJ7+eT51KZ8ya8MyGV8eCfz+czdT+iibzmC0GLOC8jVAvHoPnDAODIbBNg7BjKfcYEbOQ+HwCMLvxwnlWrVkGSJMj+KJRQ3O7hTCB66yxatAjV1eaURBTmzJmDuro6MI0hdN25WWrpBK75ABifbU8IMU91dTXq6nj54cF+591nA2MBG9FT0W7iHvadd6wp1y72s3v3bng8Hkv2mYwCNiRvlZaW6qVMfGqXqfvyaXz7e/bssbzRVDrLly/HsmXLwKAhlHButgAfG8OKFStMW0knskf8oQ6oqvP7JqSTkEMIR/mKdCMzSoSSkhK9rBPvDeNsoSDPmDC6V9TChQv5xQZjiAWdW+IrG9HRbCGzUnQ3bNiApqYmMFVGbMR5WRMi8+e2224zrewUANxxxx18QsPXDyWSf6UygLFg08GDB3l/EBPcfPPNcLlc0CIhqLGIKfswggjYmDkpH43yFcGSx5yAkCVGb0wiEev/L9etWwe32w0WDkMLhy3brzKaYSOCj3bTAzaRKFjMWZOp2mjAxoqJ90SCX99JHrfp+zKDGLcsm7tYpL6+Xm/MLnc47zpH7uDXLKtXr9YnqZxGBFaM6mMjtiMCQU5TVVWFxYsXA3BmWTQxJlHW2EySJOkl4obP5VflhuFzfGGFHSvACSEzJ8rK9nU7L2CjKEwPJIkSvXYTbTGuXFExYkHvH5HJI/ZrNQrYkLz28MMPAwBCWj8UZs6NrMYUBNXulP3ZTZIkPVgViDl3Aj4Q4xPvZvUIAHgWxooVK8CYCn/osmn7MZMveBEAn3RfsmSJKfvQez4FrltSQnCmVCWOWITfJJmRBaBnp/mdF3zIFmMMUT9/j4m/y2hut1sPnEX6Ls5sG6UVmLPpBJrWH9Mfa1p/DHM2nYC7dOaBA1WOITrEM9NEJpBZ5syZo2doRbrOm7ovM2hyArEefn4UQW4zVFdXj00WDjpvshDg7xtliGcamtlUPhYbXd3uzs8JZgCQRscej1sfKKisrNRXCltVFk2LRsGCgZRegXarrq7W+yqoA86ZTNXCEbBIFC6Xy5KJaEUZXUmZRT8iRxkdt/53mEhcD8hXnLegS77Mx2TWNYsRxHs/din3kttMURHv4Ne0TgkCpyP+P/zvX7d5JBP53+fX63v27LFkf6If4tAZZ2cKJ5MjMgJXeGDQrH6OhBBziN5p1y47r/pHT6cCRQZqa2sdk2HT2Nio97l8911zr6n6+zV0dWlwu922Xbfk6VUvIdzq1atHL6wZ/CZl2QS0HmhQsWTJEtNq3c/E8ePHAfA+NgnVutWnmUqoYUTkAUiSpJcTMoMkSXj00UcBAMP+C3nRoyWZqiXgC/CV74888ohp+9m+fTtKSkqgyCEk4s6tbR4OdUNkZZnRNFMP2Pg68+61Mp4c9UGJh+D1evX+PGa47777AADR4U6oiexX2kuSC56yKnjKxpqBi+8laeaXIZG+SwDTsHr1aktWXp48eRIAEL1+AZqSX9l80esXwFQFy5YtM/1zTGR7yf3OXEyg+oehxaOorKw0NWCjT8yamPllutH3p9lZAZOxuiya2M/KlSsxa9YsS/aZCbFiWut3zopvMZaVK1eisrLSuh07eMHJlCwct5iwlS91g6nOWbHLFBXyZT757uRJ5S1btsDr9UL1haEMh3LaVrxrCCyhoL6+3tGlqsSiF/+HPdBk57xmYr0BxPoC8Hq9ek9Fs4nXpv/yCORQflzrDZ/pB9MYli5d6og+E4SQzImS3lcvWlPiKxsdF/n1/9q1a02tZJEtEcB/x+Q+NqdO8e2vX78eNTU1pu5rMhSwIXlPZL341euGT8AyNhYIeuihh+By0Mq+BQsW6Cu+/bGrM9qGx1WGJfUHsKhuLMVvUd1eLKk/AI8rt4aF/igf044dO0yPyN95551oaGiAokYQCOdXU3Bf4BI0JmPp0qWmplpWVFToE7XhgHP7/Yix7d+/35Ttb9++nffzSUQQGcmv18p4/r6zAHj2lJkTZitXruQThowh3DuzLBujMcYQ7r0AAPjIRz5iyUXk7t27sXTpUjBFRrSr3fT9GYVpKsLX+GvlqaeeMv1YiUmVRP91MG1mn8musgrU7TuB2t1jwf7a3cdQt+8EXGW5lXOL9/FzzI4dO0xtKi/Kp7K48/pIZIrFeVk3u0rB6gGbfmt6r4lMHqdk1wgiYKP2W99LaDJqn7XldxobGwEAzIF9WTLBIvy9JP4OM23cuJGXMo0lIF9xTqajfKUHLC6jubnZce+xZMnB/NjF3I6f+P0dO3Y46h5yvJtvvhktLS3Q4gqC55zzmvG9y+/Bd+zYYVkQfd68ebwsHgP63smPbPzet3hmlFVBLUKIccR1VGeHDDnhrEUpVy7wgIXIaHEKMWd2+gMFCROPmehfY1bv10w498qBkAwdOnQIDQ0NUJFASDN29WGM+ZBgYZSXl+Puu+82dNtGeOCBBwAA/ti1GQWrJMkFr7sCXvfYBJj4PpeV74xp8Mf4ZPj9998/4+1kqqysDI899hgAYMh3Nm8yJzRNwXCAl1f66Ec/avrNnOhjE8ohYOPxVmDRijuxYNlR/bEFy45i0Yo74fHmNpGqacpohg3vs2GGsrIyPPTQQwAAX++Hji4PNxUlEUF4iJe4euqpp0zf30c+8hEAQLjvoiOOWSIwACUaQEVFBe644w5L9ulyufDkk08CAMJXT0NTnJe6nk7keju0eARNTU2WHKubb76ZN+2VE5CHZzbRLrlccFdUwV0xlpUlvpdyPE8mevn5z+yL79mzZwMAtFjU1P2YSYy9qanJlv2LgI3m94NZUJZNHRhI2a9TiMljrX9wxkFQo2l9PLhlVcBGvAa1cH6+n8S4rXgvud1uPWMicaYj6993VVeg5pk7MOvxsVKjsx6/DTXP3AFX9cyv8xJn+EKu22+/3dHBCwDYuXMnACB2KceAzaW+lO05lcvl0l8zvveyL4vmrSlH2+8cwopPjC22WvGJ/Wj7nUPw1pTPaEyMMfje4wGbo0ePTvNsY4kqFr1vOjNTOJkcSmDoQ34+NqMPKiHEXAsXLkRTUxMUeSyjxQkYY7h4lo9n8+bNNo8m1Q033IDm5mbE48CZM+ZkJkWjDGfPUMCGkJyVlJToJXuMLovmG93esWPHUF1dbei2jbBv3z4erNLiCCWsWYGaiVCiFyqLo7Gx0bLVPg899BBmzZqFhOxHIDSzjCOrjQTaoaoxzJs3z5KJ1P3798PlciEeG55xWTRJcsFbUgVvydhEqvg+lyAfAISDXWCagpaWFlNLXD3yyCMoKSlBIjyEWNA575tsBPp4YHLdunWmlnUSDh8+jPLycqixIOSQ/X0UIgNXAPCAfVVV1TTPNs5dd92FBQsWQEvEEOk8a9l+Z4qpCsKX3wcAPP/886ZmlAhut1sPuMY6nNXvRx7uhxoYhtfrNb0evpiYZdH8nGAGxsYugk9Wa2ho0JucqoPmZpcwRYY2MgzAeRk2q1at4llOsgytL/uFSVJlBcrvvxNlJ8YmPctOHEX5/XdCqsx+Ap5FY9BG++lYVSq4ubmZ7zuUfVlOJxDjFn+H2cSEc+LidWix7Mo6SS4X3DVVcNeMZe66ayrhrpl5wFyLJZC4OHZP5XSifG78ygCYMrMSYWooBrmH9xURpUKdTARF/B/2QI1nNwEmuV0oqa9ESd3Y+aSkrgIl9ZWQ3DN7zUSv+xAfCKG0tNTyzJE77rgDkiTBf2kE0UHnlR1P1vdON5jK0NbWhhUrVtg9HEJIliRJwi233AIAaD/jnDKMA30q/CMavF6vZYtzMiVJkh5EefttcwI2H7yvQFF4QM2sHtOZoIANKQgPPPAAJElClI1AZsbczKksgZDGV6w8+OCDhmzTaCUlJThx4gSAmZdFM4Moh3bixAl4vV5L9jlr1iw888wzAIAB3wfQmHNqMKejqgkM+c8AAD7+8Y+jpKTE9H02NDToKaS+IWdNpAJjY7rnnntMLdvU0NCAe++9l++z+33T9mMWVYkh0M+P1bPPPmvJPisqKvTXjgiW2IUxDdHBDgB8pa6VvF4vXn75ZQBAuONDaHJuq/7dpRVo3HECDVvHJrAath5D444TcJfmlrEGAOFrZ6ElomhtbdUXNlhB9PtJ9FyDGsmtB4CRopf5Off48eNoaGgwdV96hk08fwM2dmfYAGPBE7P72KhDQwBjaG5uRktLi6n7ypbL5dInkNXO7Fe/Sy4XXNVVcCUFt11VVXBVz2wCXunimbBtbW2WBSBEaV0WcM75JBti3FY17V2zZg2fvFVUJM7af3+QOHsVUDWsWrXKkp5zuVq1ahUaGxvBZAXxazMLFovsmra2NtuC3tlYs2YNFi9eDCar8L1jf8ng4d90AOCLE61cmAPwwKoIRvf81tlZNj2/4VnDlF1DSP7aunUrAODMe84J2Jw5xceyfv16lJXl1irBDKJ8/jtvy9A046t/vPUWzy7at2+frf17KGBDCsK8efP0C6uAakzt3aDWD4CvWHHyzYWYhAsn+iCr9k8MyWoEYZlPrIhJcas8+uijaG5uhqJE4AtcymlbXk8FlrQexeJ5R/THFs87giWtR+H15D6ROuw/C02TsWLFCktXG4rScQHfZaiqcy4KYtEhxCID8Hi9evktMz3zzDPweLyIBXsRC1rT0Noogd6zYJqCtrY2S1N0xerL6ECHrWUH475eaHIMtbW1lq3uTnb06FGsWLECTEkg3HE6p21JLhc85VVwlyeV/iqvgqc899JfmhzXx/fSSy9ZEhQWVqxYMdpjjSF6xRmZSGokhEQPn4R6/PHHTd+fmKBjsagjygjOBItZ13djMiKDUB0yN8NGlENbv369oxqrCiIjTJlBwMZoImhkdpZaMhHo0PxBy/ZpJM1vbcBGkiTcc889AID4B5ct2edU4u/zMdx7772OfH+NlxwkjbX3zGgb4vdEE3unkyRJv/4e/O8rtn5uqVEZI6NBI7sWTd51110AgN7fduV0LEpry7Dtf+zDLb839jq45fd2Y9v/2IfS2twmQcN9IQQ6fHC73XmRuUYISe/WW2+FJEnovKxgZCj7Rce19S58/k8a8Jkvj/Wc/MyX6/H5P2lAbf3M7ic/eJsvShQl9Z1my5YtqKysxMgIw+XLxi7UVhSGU6d45o5ZZfozRQEbUjDEypKg1mvIRWZQ5RfaoqyAUy1atEivKxmI2b8iSvSu2bJlCxYtWmTpvsvLy/Gxj30MADDkPwNNm3mKpCS5UOKtgtczVhLC66lEiTf30l+KGsNwgDdMf+WVV+B2u3PaXja2bNnCJ5s1Bb6hc5btdzrD/R8AAG47csSS1dxz587FiRO8L9Vw19t5M6GqJCLw9/EJ8BdeeMHSiY/du3ejpqYGaiKC+MjMJjCMEO5tBwDcdtttlgYhBLfbjU984hN8LNfOQo05s1xGuOM0mJLAihUrbPkce+KJJwAA8asXoMXtbxQevXgaAMO2bduwcuVK0/fX1NTES9BpGrTgzEpQ2k3183I+oiyZHUQZBnXQ3P4t2mhAyGllH4QdO3bA7XaDjfih+ex7PTFFgdrJM2xE1qUVli5dCkmSwEKRvOtjw2QF2pAPALBs2TLL9nv8+HG43W6ovcNQhwKW7Xc8ddAPtW8YHo/Hsp5zRhCBlmh79gsBmabp/W/yJWAD8MoIZWVliPX4Ebk6bNs4Rk51QkuoWLZsmV4qyGoHDx5EZWUlooMRDJ+ZeY9cl9uF8oYKlNePLfYrr69AeUMFXDMsFydc/2UHAN4jKR+yuAgh6TU2NuoLlESgJBtut4SG2W7UN47NKdU3utEw2w23O/u5glBQw6VzPMNEZLI4TWlpqf75+tZbxpZFO39ORTjMUF9fb/t9AQVsSME4cOAAysrKILMI4iy3GxOZRRBjfrhcrry4uRBZNr7YVVtXvjOm6QEbK8vvJDtx4gTmz58PVY1hJNBuyximM+Q7A8ZUrF271vIPQUmS9KDWyOAZKIoDJlIjAwgHu+ByufD8889btt8XX3wRZWVliIcGEBmxP9iZiZHr74JpCm666SYcOnTI0n2XlpbqgXERNLGaKscQHbL3HAPwicqNGzcCmorgxXdsG8dklGgQ4Ws8sPeJT3zC0qCwsGvXLqxZswZMVRC9lFsmUq7USAixa/w1+8ILL1iyz+Saz8pgfmXxAYCWSED18Qm7jRs32jaOZcuWobKyElAUaH5zAhWMMb1Hzs0332zKPnJVW1urr/hX2u3LmFA7OgFZxvz58y09VjU1NWhra+NjuJ5fvefU3gFA0zBnzhxLg58NDQ1jvVg+tK+Uqdj37t27eS+mPLFjxw64XC4ogwEovuwWZiSuj0CLJlBdXW37ZE82ampqcPjwYQDAyDudto1DZNfYmZFVUVGhX2de/OezYCaU3MlFdDCCrl/xcoeiDC0hJH+JOaHTMwjYGO3MqTgY4yU9W1tb7R7OpER/s1PvyIZu951TfHt79+615R46GQVsSMGoqqrSU9YCam6rv0VZta1bt1pWnzsXhw8fRn19PRQtikDcvnIZgXgXFC2KhoYG29IHS0pK8NJLLwHgWTaKav+HXrKEHIQvcBEAz66x40bkyJEjfCJVUzAy8IHl+0/GGMNQ7ykAPNhm5erTOXPm4KmnngIADHe9A6bllk7rKalA69q7Me+GsSDvvBvuQOvau+Epyb2MXiIygtAgL/X3u7/7u7a8du6//34AQHT4GtSE9aucI32XAKbhhhtuwJo1ayzfvyBJEj7zmc8AAGI9l5EYcdYEYvD8m4CmYsuWLZaugk8mSRI+/vGPAwCiHef0fih2iLS/DzAN27Zts3S17qZNmwAASr99GWkzpQ7y1/TChQttvQ5yu936qkOl25zrG214GCwWQ3l5uaNL4N59N88KVdqvmJptNBURLLrzzjvhyrFsY7a2bNkCAFA7jSl9bBUx3i1btlj+uS0WWSQ+7LDlNcM0DYkzV1PGki9qamr0oKTIlsmUeP62bdvg8XiMHpqpxEJF/3vXwVTrXzPxoTAiHcOQJEkvxWuX559/HtXV1Qh1BdD7hrN62Vz6l3Ngiobt27dj586ddg+HEJIjEbC5eFZGNGLfAmwA+OAdXjZfBEScateuXXC5XOjs1DA4aNwxe3e0HJpd99DJKGBDCspYWbS+GWeaMMb0cmiifq3TlZWV6eVnhiMXbCnvxBjDUISX+XryySdtbU527NgxtLW1QdNkDI7YG5AYr3/4PTBo2Llzp20X2JIk4ZOf/CQAwD/cDkWO2DIOAIiGexGN9KcE2qz01FNPobGxEUo8iED/+Zy2JUkueEur4Ckd60fiKa2CtzT3MnqMMQx1vgWA4fDhw/oEptVWrFjB980Ygp3Wvrc0VUGw60MAsKTP0XRuvvlmffVl4NxvbZtAHS820In4QCc8Hg++8IUv2NovYPfu3XzCS1URuWjPuVgNBxHv5EFyEUCyikjVl3uugynGrv4yW6KrAwD0Ffp2EgtAlE5zMiHFdnfv3s3L2DnUrbfeilmzZoFFItC6rQ8Sa+EI1G4+EW3H5Lu4cVYuXQOTjS1/YRamaVAudACw58Z///79qKurgxaMQG63fsI5caELWjCChoYGS3vuGUVcp8cuZvd+EwGbfJxI37ZtG18EGI4jcNb64OjIWzzAd8stt9i+aLKurg7PPvssAODSj85BTRjbJ2GmAld96HuLL2D41Kc+ZfNoCCFGWLx4MZYsWQJVBc69b1+fYUVm+v6dWg5NqKur07NYjcqy6elR0dOjwePxjPZjtRcFbEhB2bZtG2bPng0NMsLazBrUxpgfMqKoqKiwvclUNh566CHMmjULCTWEoA1ZNsH4dchqGDU1NbY1iBTcbjd+93d/FwDgC15CPOGM/gGRaD9CkS643W589rOftXUs27Ztw6ZNm8CYhuGBD20ZA2MMQ/3vA+AT8C0tLZaPoaqqCq+88goAwNf9PlQHlIhLJ+q/jligB16vVw+22eXll18GAIR6zkOJhSzbb6j7LDQ5itbWVn2lud0++clPora2DkpoBJGu3AJ+RmCaiuD5NwDwHjLLly+3dTySJOmvl9i1dlt62UQvnQYYw86dOy0PdN500028ybiqQO5x1urcqTBFgXydl8NxQiPjAwcOQJIkaEND0ELGnnMYY5Cv8QlCUQrIqUpLS/UV58pF68uiKZeuAIxhw4YNtvQ1uuWWW3hpjoQM5bJ95ZqyoXb1gYUiqKmpsWXio7S0VL8mj719wfL9x9/mn4sPPvigo4OhkxGTNfGrAxkvhtMSChLXh1N+P594vV7cc889AIDB/7b2PKMpGoZ+w0voPfDAA5buezInT55ES0sL4iMxdP3CvtKCAmMM7f9wBgDvU+XkrFBCSHbEwo4z79oXsLl4TkYiztDU1JQX5xeRBfTW28Ys5BH9cG655RZUV1cbss1cUMCGFJTkhpZBbWargkR2zcGDB1FRkXsZI6tUV1frWTaDkXOW9rJhTMNgmDewf/LJJ1FVVTXNb5hvy5YtozfHDAMj79s9HDDG0D/8LgBeVmrFihW2jkeSJLz44osAgMCIPVk20XAvYpEBlJSU4KMf/ajl+xfuuecerFq1CpqagK/bWRlZAH9/DXe+DYDfONrZABzgExDbtm0DmIbAtXct2acmxxHs4n1QPv7xj6OkpMSS/U6nrq4Or77KA36hS+9CS9gb8AtfPQM1GkJTU5NlvVqms2PHDtx4442AqiB65ayl+9ZiEcRGs2us7I8lSJKkBzwS1+yf6MmU3NMFqIrlfUom09jYOFZersvYiXrN7wMLBlFSUoI9e/YYum0ziMwWpeMamGxd1hZjTC+HZlf2ucvlwokTJwAAyln7+vhkQznLy5geO3bMtoDFQw89BI/XC6VrAEqvdY3klZ4hKNcH4fF6bV/INVNr1qxBWVkZtEgcykBm/VETXUOAxtDS0uLo2v9TefDBByFJEkIX+hHrD1q2X//pbijBOGbPnu2YRZNlZWV49dVXAQAd/9YOOWTfRCoADJ3uh699CCUlJfq4CCGFQVyHnnkvDs2mvlln3uXtBHbv3m1rlYZMiYDNmQ8VRCK5H7N33pZTtms3CtiQgiNWXoe1Aagsu5tZjakIan0p28knjz32GOrr6yGrYfhj1jVR98euQdbCaGhocFTjw09+8pNwu90IRa4jErO36XMw3IlYYhgVFRW2lP5KZ+vWrdi4cSMY0zAyeMby/Q/38+DI/fffb3uPBNGPJNB/HnLcupvTTIQGL0GO+VFTU2PLpHM6n/jEJwAAkb7LUGLmH69QzzkwJYEVK1boQXmnuO+++7By5UowJYHQ5fdsG4cajyJ8hQenP/nJT/JG7Q4gSRKee+45AEDsyllosnWTHdHLZwBNw8aNG/UJf6vddtttAAClrxvMwr89F3IXzzg5cuSIY27WzCqLpnTyAND27dsdsdhkOuvWreNBe0WF0mFdlok2NAI24kdJSYn+mrbD8ePHAQDq9T6wmLN6FI7HVBVKB894tzNTrampCUdGs8dip9ot26/Y1+233YbZs2dbtl8jlZSU6OVW4tcyq9wQvzoAgPcwc8r5M1utra16CbshC7Nshn7NA5z333+/YxbmAGOltpWogo6fXbRtHExjuPQvfIHkyZMnMW/ePNvGQggx3oYNG1BVVYVQgOHaJetLvzLGcPodfm2VD4uYAGDp0qVYvHgxVBU4fTq3YxYKMVy4wEtfOqUcHAVsSMFZtWoV2trawMD04EumwtogNCiYM2eOpY2JjVJVVaVPjA1H2i3JsmFM03vXPP/8846ZJAT4CVz0mOgfeteW3j4ADwQOjPCJXNEzxQkkSdIDAP7hdigWlgOLhvsRjfTD4/Hi6aeftmy/k9mxYwcvXcE0jHS9a/dwdJqmYOQ6f+0899xzmDVrls0j4tauXYsdO3YAYAhdNzdrgmkqQt38BvW5556D2+02dX/Z8ng8+NznPgcAiHSdhxLy2TKO0MVTYKqCG2+8UZ/UdIr9+/dj6dKlYIqMWIc1peO0REzfl6hBb4cVK1ZgyZIlgKblRVk0psiQe/k47ZyYH+/AgQMAAHVgAFosath2RcDGKau5pyNJkp7holy4ZNl+lXa+r/3799v6ObRgwQKsXLkSYEwPhjiV2tUHyAqamppw00032TqWRx55BACQOHsVWtT8QJcWiSNxlgd+H374YdP3Zybxf5foHsno+eJ5TshOzIV4zQy/eRVqzPxsvuh1H8JXhuDxeBzRpzCZ2+3Ga6+9BgDo+mUHEkF7gsUD7/UgdD2AqqoqPPPMM7aMgRBinpKSEr0s2rtvWF+14fpVBUP9GsrKyvKqB5vot/n++7kFbE6fVsAYsGzZMsydO9eIoeWMAjakIInJqpCaXVZFSOPPP3bsmOMmBTP1wAMPoK6uDrIWQTDeY/r+gvFuKFoU9fX1uP/++03fX7ZeeuklVFRUIJYYRiB81ZYxjPgvQFbCaGpqwpNPPmnLGCazc+dO3HjjjWBMhW/QunJFwwM8u+bEibsd84EoesOEh68gEcnsxtxswf7zUOUIWlpa9Jtnp3j88ccBAOG+i9AU8zIHIgNXoMkxzJkzx7E9JrZu3cpX4jCGwIW3LN+/HBxGtJuvZv7c5z4Hl8tZl3cul0sPmkQvnwFTzF81Fr1yDkxV0NbWht27d5u+v8lIkoQjR44AABKdHbaNI1Nyz3VAVTF//nxH1a6eN28eHw9jUK8bM1GvhcPQRobhcrkcU/ogE3fddRfv6dPTBy1gfoYjU1QoF3lJP1GSzE4ieKdccnYfG+USzwbbt2+f7efkdevWoa2tDVBUJD40vzxj4sMrgKrhhhtuyPvAhTgPJnoyDNiMPm/NmjWmjckK27Ztw5IlS6DFFYy8bX7VBtEv59ChQ7Zm3U9m9+7dWLt2LbSEiqv/bl2wXGAaw5Wf8AWSjz32GGpray0fAyHEfOKe4b034pYvNj7127FyaPnUGkIEl95/T87pmL3/vpyyPSdw1h09IQYRkekY80Fjaka/wxhDVBtO+f18VF5ePrYqKtpu6omeMYbhKE8NP3nyJMrKykzb10zNnj1bzzoaGH4PmmZteqmixjDk4+XGXnvtNUdlIAF8MvFjH/sYAMA3dM6SXjaRUC8ioR54PB5HrRBbs2aNfpE0cv1dewcDQFNl+Hp435aXXnrJcQ17d+3axbMmVBnhPvNKRIgMnkceeQRer9e0/eTqM5/5DDweLxJD1xEftG7lN2MMwQtvAgBuv/12bNiwwbJ9Z+Po0aNobW0FS8QQ6zS3LI+myIiN9st57rnnbC9Lc/vttwMAlN5uaAlnl0WTR4NKt912m+3HbTwRVFG6jMlUEtvZsGED6uvrDdmmFebNmzea4Qgo580vz6N2XAPiCcydO1ffr53E57R6tRss7sz3E1NVPaDkhEw1SZL0jPP4aXMDNowxxE/zyff777/fceeRbInAi9znB1OmvqdUg1FooRhcLhdWrVplxfBM43K59EwXswM2mqzC9x4/Hzstu0aQJEkvad31yw7IYWvPPYOn+xDqDqKqqgqPPfaYpfsmhFhn165dqKiowPCghqsWlkVjjOHd0YCNuM7KF5s3b4bX68XgIEN398wqDDHG8P57/HhTwIYQky1ZsgQtLS1g0BBlvox+J86CUCGjsrIy71eDPfzwwygtLUVc8SOmmJcpEFWGEVf8KCsrw0MPPWTafnL1xBNPYN68eVDUKEYC1tXvBoAh3xloTMYNN9xgW6Pe6dx6663YsGEDGFMx1P++qftijGGw7x0A/KZswYIFpu4vWy+//DJcLhcivk7EQ5nVKzdLoO8sNCWOhQsXOvK1I0mSXuok0nfRlOBwIjQMOTwMr9erTzY51aJFi/DoozxYHrx0yrJVUYmRXiSGe+H1evGpT33Kkn3OhNfrxUc/+lEAQPTiaTDVvJuQ2JVzYHICS5YswaFDh0zbT6ZWrFiB5cuXA0yD3G1df7lsMTmhl0NzWq8oAHpPBaWnG0zNbDHOVJTrnSnbzScio1m5cAlMM7f8rTwaFLrnnnsckX2+cuVK/n7SNMdm2ajXeoB4Ak1NTdi8ebPdwwHA39Nerxdqvw9Kn3n3BmrfCNQB3u9IBKvzWWtrK6qrq3lZy8GpM9oSvT4AwOLFi/NqdfJkjh49yq+Jr40gPhgybT+Bs73QYgpaWloc835JZ/fu3Whra4OWUHH9V9ZWbbj2f3lWz4MPPkjZNYQUsLKyMv269N3fWlcW7fpVBYN9KkpLS/Nu8XpFRYX+2SGCLtnq6tIwPMxQVlbmqNYYFLAhBUmSJH0VYGQ0a2Y64nm33HKLo1dxZ6K+vl6fbBmJmreSzje67WPHjjn64rG0tBQf//jHAQBD/rNQVWtWRclKGL4gn+j41Kc+ZXtJjMlIkoRPf/rTAIDAyCXEYz7T9hUKXEM8OoyKigo9s8dJli1bpjcHHu56x7a+R6ocg6/nQwA8iOTxeGwZx3TEBJAcHoEczuxcm43IaObO/v37UVdXZ/j2jfbMM8+gvLwcSmDIkiwbxhhCl3iPowceeMDxDWhPnDiBlpYWaLEIolfOmbIPLRFD9CIvufjCCy84YoIZGAuAJDqsL6WSqURnB6BpWLp0Ke8T4jA33HAD5syZA6gq1N7enLbFZBlqH+9zmI8Bm3379qGxsREsGoN6zbxzjeYPQuvpgyRJuOeee0zbT7bE+0k5P/NrXKmqAhWPHkf5g0f1x8ofPIqKR49Dqsptsl2M67bbbnPMOai2tlbPUkuc7TBtP6J3zf79+1FTU2PafqwiSZKeLSP3+aZ8rtznBwBHnj9nYvbs2di6dSsAwPeueT3YfKd44PWOO+5w7L0SwF8LTzzxBACg8xdXoMm5LxzIRKDDB9/FYXg8Hjz66KOW7JMQYh+RmXvqt9aVRTv1G55ds2fPHsdVhMmEXhZthn1sRKDnlltucVRVE+d+IhKSo40bNwIAYpo/o+fHGH/epk2bTBuTlcTK92D8OhTN+OaIihZDKN4NAI7OrhGOHTuGpUuXQtMSGA6YM1E43uDIh2BMwy233IJt27ZZss+ZWr9+/WhdeIahvlOm7EPTVAz18m0//fTTaGhoMGU/uXrllVdQUlKCWLAXUb89TY193e+BaTwz6+jRo9P/gk1qa2v1fgLhXmNL8zBNRaSfl1Vx0kThVBoaGvRzb+jyu6ZfZCdGeiH7+uD1eh1VXnAypaWleOWVVwAA0YsfQEsY/9kUaf8ATJHR1tbmqCyRO++8E5IkQR3sgxoMzHg7rvIKVB+5G9UHxv626gN3oPrI3XCV5zbJnOjg7+F7773XkWWMJEkay7K5ntvkodLTDWgaFi1ahKVLlxoxPEt5vV7cfffdAMwti6Zc4NvetWuXY/rNAUnvp+t90Pwz6+MjuVxwzaqCq3psYsJVXQnXrCpIOUwas2gMymX++hT/R04hMl4S5ztN+XxijCFxnk++O/naJVttbW0AgESPb8rnyb08cynfy6ElExOH/g/MuR7WEgqC5/pS9uVkt99+O5qbm5EIxNH3drcl+7z2H/xa+OjRo3zRAiGkoO3atQuVlZXwDWnouGh+WTTGGE79hmfz5MN5OB0RsDl3ToGiZH99c/o0P87bt283dFy5ooANKViirFmcBcDY1OUiGGN6YCffy6EJa9aswU033QSAwR8zvgSLP3YNDAw333yzoxoTT8btduPVV18FAIz4L0BRzU0xTchB+EN8heVrr73myMmv8T75yU/C7XYjHLyOSCi31cvp+IcvQJZDaGpqwpNPPmn49o0yb948nDx5EgAw3Pn2tOcPo8mxAAIDvLHoZz7zGUevNgTGgimRgctgmnGrDaNDndCUOJqbmx3RNyFTTz31VFKWjXkrUnl2zbsAeHmkfLmJP378OFauXAkmJ/RMGKOokRBiHTwg77Ssxrlz52LXrl0AxgIjMyG5XHBXVsFVWaU/5qqsgrsyt0lm1T8CdXgQHo/HkSUYheSATS4TziLgk4/ZNYI496pd3dDCxvefY5oGpZ1PFN57772Gbz8Xyf105LPOylqTL3QAmoYbbrgBq1evtns4Kfbs2YPy8nJo/jDUXuOzYtXeYWiBMCoqKvTzXSEQ9zkiIDMZEdARfW8Kwf79++FyuRC97kd8KGz49oMX+qHJKubNm5cX95MlJSV6r9jOn182fWFOzBdF/zs8MCSyewghha2srEzPiBWBFDNdu6xgaEBDeXl53pVDE5YvX476+nrE48DFi9nNRygKw7lzPGDjtEXWzrmTJcRgixcvRnV1NRg0xNnUdXcVxKEiAbfbXVAX2aJxoz/aYegFJWMM/igvefDggw8atl2zHTx4EGvWrIHGFAz7zpq6r8GR0wAY9u7di/Xr15u6L6MsWbIEDzzwAABgqM/Y7ABNlTE8wCdnX375ZcfX9n7uuedQW1sHOeZHsP+CpfseuvYmwPhrR5SicLLt27fznmFKAtFB44LD4dFyaHfffbdjSspkIiXL5tJ7pt3MJ4Z7Ifv6UVJSgmeffdaUfZjB7XbjE5/4BAAg2nHe0Cyb6OUPAU3Dli1bHBnkO3HiBABA7jT2M9kIic4OALxGv1OzHwFg69atKC8vB4tEoPlm1oeDMQb1Ol8tnq83pgD/zN64cSPAmB5YMZLa2Q0WiaK+vt6RgS0RsFLOXXHU+0k569zM0PLycv3/Mv6B8a+Z+Ps8eLZ//36UlZUZvn27iEBCoscHpqV/rWkxGcpQMOX5haChoUHvDeB7x/gFgCNv820ePHgwLxa3AXyRTFlZGYKdAfguGR/4THb9PzvANIZNmzY5LgBMCDGPyIh997dxaJN87hjlndd5UOjWW291/BzNZFwulz5v8uHp7LKSLl1SEY8DdXV1WLFihRnDmzEK2JCC5XK5sGHDBgBAVJv6pj462r9m9erVKC8vN31sVrn99tsxa9YsyFoE4US/YdsNJ/ogaxHMmjUrr9ImJUnSy/GMBC9CUaKm7Cee8CMQ5gEt0TsnX7z44osoLS1FLDqIaNi4LBv/SDs0NYFFixbpk5ZONmvWLLz66uhr5fq7UBVrmv5FfNcR9V+Hx+PF7/zO71iyz1y53W79/zTc127INpV4GPERPqGaD6+X8Z566ilUVFRACZqTZcMYQ+jyuwB475p8ya4R9uzZw0vGqApiV40JiGqJGGLXeJDv2WefdeTEz969e/nq9kgI6siQ3cPRMcYgd3UAcH4Zo9LSUr0Z6Ez72Gg+H1g8joqKCv06MV/pWTaXOwzftjK6zWPHjqGkpMTw7edq//79qKqqAgtFoHUbd42bC23YD21wBB6PR59scRqxMCf+YQdYXDZsuywuI37maso+CsXSpUvh9XrB4jJUX/osE9HfZs6cOY4Oes+EyLAbfP0KmGpc1nliJAL/6Z6UfeSDuro6HD9+HABw7WfmZfgpMQVdv+Lvqccee8y0/RBCnGfHjh2oqamBf0RD+xnjPqvHU1WGd17ni+ecfg8wHRGwOXMmu4DNhx8q+u87qToDQAEbUuBESltEm3r1i/i502oW5qq8vFyf7PTFjFtJNxIdK5GRbyvodu3ahfXr14MxFUN+c7JsBn2nAQCHDh3Ku1V2jY2NuP/++wEAwwMfGrJNTVMxMngGAJ9I9Xg8hmzXbPfffz9WrVoFTU1g5Pp7pu+PaRqGO98CAJw8+SgWL15s+j6NIs4zcV8PlOjM+gkki4z2w9m8eTMWLVqU8/aslpxlE778vuGrvxMjfXp2TT70rhlPkiS9LGL0ylkwNfdSerGO84CqoK2tzbGf5cmlBhLXZt4s3WjqyBC0UBAlJSV5kXGyZcsWAIAyw4CN2ssnCDdu3OjIQEQ29u/fD7fbDW3YN+NeLukwVYV6jQfNnbowp7S0FIcPHwYAyOc77B3MKPk8f1/v3LkT9fX1No8mvS1btmDJkiWArCB+9qph242f6QBkBUuXLi2YfqCC1+vVV90men1pnyMeF/1uCsnhw4fR0NAAJRCD/7RxfVuGXr8MMIYtW7Y4blXzdJ566im4XC4Mnu5DsCuzfrnZuv7LDigRGUuWLMH+/ftN2QchxJlKS0v1hR9v/pd5C0cvfJhAwKehtrYWu3fvNm0/VhD3B+3tKuLxzO+9z4wGbMTvOwkFbEhBE5M2UTYCbZI+FIyxgg3YAMDDDz8MSZIQTvQjoUxdGi4TCSWIiDwASZLw0EMPGTBCa0mShJdffhkA4AtehGxwlk084UMwzBuuvvTSS4Zu2ypPPfUUPB4PouFexKK5rwIP+q9AVWJoaWnBsWPHDBihNdxuN373d38XABDsv4BE1Gfq/gL95yHH/Kivr8fHPvYxU/dltPnz5+slqMK9uWVMMKbp28jnVbpPPvkkysrKIAcGkRg2tjFt+Mr7AHjQvLm52dBtW+Xo0aNobm4Gi0cR784teME0DdGO8wD4+cuJ2TWCCG4mOi6CyQmbR8PF23lA/fDhw6iqqprm2fYTK+jU/j4wLfvV3iLQkw8lJ6dTW1s7FsC6aly5IrW7F5BlNDU1Obq3o1jlrly4Aha1JhN2MkxRoJzhiw3uvPNOW8cyFUmS9GyGxJkOw7abGA3+3HvvvY4+B8+UCMTIkwRsRIZNIQZsSktL9cVcI28Zc55hGsPI2/x+KR/vJxcvXowjR44AADp+OvO+dJNREyqu/QdfIPnss8/mVWlgQogxxLXEe2/EEY+Z01P3zV/xa6ejR4/m/SKmhQsXYs6cOVAU4ML5zLJsEgmG9na+cJACNoRYbMWKFWhsbASDhhjzpX1OgoWhIoHS0tK86TWSjYULF2LPnj0AxjJjciG2sXfvXixYsCDn7dlh27Zt2LBhAxjTMGxwls3gCM9KOXz4MFauXGnotq0yd+5cfdWqfzj3m5DA6DYefvjhvLsQ2Lp1Kw4ePAiAYejam6bVyVeVGHzdPIvn1VdfRXV1tSn7MZO44Q73XQTTZp4xERvugpqIoK6uDocOHTJqeJZraGjQA05G9rJJ+PqRGO6Bx+PBRz/6UUO2aQev16u/ZnIti5bovQYWj6KxsdGx2QDCrl27sGzZMkCREb9sTAnBXGiREOQuPtEqsp6crq2tDXV1dYCiQB0czOp3mapC7e8DUBgBGwCjn1GAeqXTsG2qHXxbBw4ccFx5iGRbtmzhfR0UFYn3re03N5585hJYNI558+bp/ydOJVbtKl0D0IKRnLenBSNQugZStl1oxDW9PBBI+3O5P5DyvEIjSuUEL/RDjea+2CBybRiyP4rKykpH9sjKxHPPPQcA6D/VjVC3cRmOAHD9v64iEeDnkzvuuMPQbRNC8sO6deuwaNEiJOIMp35jXM9PIRLW8P6bfLt333234du3miRJY31szmQ2F3HhggpZBpqbmx1Z3cS5V+CEGECSJOzcuRMAENHSZwqIx7ds2YLS0lLLxmalxx9/HADgj12Dqs38IlvVEvDHOlO2mY8kSdKzX3iWTe43qwAQS/gQjHSmbD9fiZV0Qf8VaOrM66bGoyOIRQfh9Xod2YA3E5/5zGfg9XoRC/QgGjA2U0LwdX8ATU2gra0tr+p4J9u7dy+am5uhyTFEB2deZiXUfQ4AX6Wb7+fkp59+GqWlpZD9A4Zl2YQunQLAL6znzZtnyDbtcu+998Lj8UAZGYDin3njXhHwue++++D1eo0animSy8HF28+AqdnVWTZa7PyHAGPYunVr3pTwdLlceka02pPd+0odHAQUBfX19QXTwPnAgQOQJAnawCC0cPr+GtlgmgblKr/Wc3rQXJIkPPvsswAA+f3zYAnz6rxPhaka5FN8AdDTTz/t+PPQ3Llz9f5NRpRFi49m6mzatAktLS05b8+Jli5dCiB9wIYxpj8unldoVqxYgWXLloGpmt53Jhe+93h/v3379uXttd6qVav4OZIBV/7VuICxmlBw9Wd8sdtzzz3n+PMJIcQckiTp8ye/+YXxWcRv/3cMsswXGtx4442Gb98Oeh+bDzO7v/owqRyaE7ODKWBDCt6uXbsAAOFJAjbicfG8QrR161a0tbWBQYUv1jHj7fiiV8CgYvXq1Y5MGczGtm3bsHHjRjCmGdbLZmiE9645cuRI3tViHm/Lli1YvHgxmKYg4J95uSL/CL+B2b9/f942YV2wYAFOnjwJABjufBtskvKKMyXHggj083JOn/3sZ/O27IHH48GDDz4IAAh2fTijjJJEeBhxXw9cLhc+8pGPGD1EyzU3N+vHJHTp3ZyzbOLDvUgM98Lj9eKFF14wYoi2mj17tr4SPXb1/Iy2oYb8kAf5ayZfSugdO3YMc+fOBYtFEb9kX1aAFg4hcYVn+YiVwvlClGBUsg3YjD5/x44djs4cyUZTU5M++W5Elo3W2w/E4qirq8PmzZtz3p7ZDh06xFdFxhOQ35/ZeSRXyrnLYMEwGhsb82Zxyl133QUAiL97MafPJqZpiL/r/FJwuVq2bBkAQBkOgimpK3fVYBQsLsPlcjlyha5RRKbH0G9zK2OqyapeWi3fs0defPFFAED/O90IXU+ffZWtrl/y7JrW1la9jCohpDjdfffdcLvduNIuo6/b2EVev/1PHgQqpFKmYo7y0iUVkcj01zYfnnZu/xqAAjakCGzfvh2SJCHBQlBYanaJxlS9VJrIxClEkiThiSeeAMBLmmks+3JFGlMxEuUX6E888UTen9STe9n4g5ehqLmtWognAghG+GqxfOs/ko4kSfpEs3/owoxu5lU1gcAIL6EnGrDnq+eeew41NTWQoz6EhnIvLZhspOsdgGnYtWsXtm3bZui2rfbggw/yvi3hYcT92TcED3WN9dKYP3++0cOzxTPPPIPy8nLI/kEkhnNblSp619x37715n10jiMBc/PoVMCX71fGxa3yicPfu3Zg7d66hYzNLaWmp/jkRP/cBmGxPVkDs7PuApmHLli15d+4RGTba8DBYPPMyEUpvT8rvFwqRCaN05N5fQrnCt7F//354PJ6ct2c2t9utB7ATp85anmXDVBWJt/iCnY9+9KMoKyuzdP8zdezYMcyaNQuaLwT58sw/m+TLPdD8YdTU1ORVn8JstbS08FKMGtPLnwly9wgAYPny5XmbLZIJkRUb6RhGtNs/4+343uuCGpUxb968vF8wuWrVKr2XzeUf5x4wVmJj2TXPP/88ZdcQUuSampqwe/duAMZm2XR1yOi8osDr9RbUZ/fcuXOxaNEiMAacPTN1gCsSYbh0ic+LOvW+gAI2pODV19dj+fLlAICYlnpxGWdBMGhobGws6BVRAK8pPWfOHKhaHIFYV9a/H4h1QmVxtLS0FEx96i1btmDt2rVgTMVIILdeAsMBXsZp3759eZ9dI5w4cQLl5eVIxH2Ihvuy/v3AyGUwpmLFihV5sUp3KjU1NfoKdN/193Lq0ZIsHh5CeOQqJEnCJz/5SUO2aae6ujp9NWCo68OsfleNRxAZ4MGwp556yvCx2aWxsVEvMRjuOD3j7ciBISSGe+B2u/HMM88YNTzb3XLLLViwYAGYIiPek11pHqapiHXyiY377rvPjOGZ5u677+Y3FIk44u1nLN+/GvAjcfUSAN43K9+0tLTw0kOMQenL7POJxePQhnhWtVNvzGZKBGy03n5o4ZmXeWWapgdsRC+7fHDHHXdgyZIltmTZKOeugAXDmD17tr7QJR9UVFTo2UDxd2d+DSyya+69916Ul5cbMjYnkiQJa9euBQAkulOrNsSv85Ke4ueFqqmpCfv37wcADP1m5lk2Q7/pAMDLL+drVnmyl156CZIkYeC9XgSu+nLaVufPL0MOJbBw4UI9C44QUtxEufQ3fhWFqhjTE/U3o9k1+/fvR319vSHbdAqxCO306akDNmfPKGAMWLRokWMX/VHAhhQFUSpCZNMIUc2n/zzfM0amU1JSgsceewwAMBLNrvwBYwwjUT6x8/jjjxfMah9JkvSJYV+gHZo2szRTRY0hEOwAwGuXF4pZs2bh+PHjAIDAyMWsfpcxhsAInwB4+OGHC+L99fDDD6O5uRlKIozggDHNwkeuvwuAN3Nta2szZJt2E/2tYiPXocRCGf9euK8dYAwbNmwouEmPJ554Ah6PB4nhHsj+7JqkC+GODwDwiclCya4BeD8ScSMSu5rd+yrR1wWWiGH27NnYs2ePCaMzj8fjwSuvvAIAiF34EFosaun+Yx+eAhjDrbfeivXr11u6b6PofWx6M8sOUPp41t/SpUsxZ84c08Zlh7lz52Ljxo0AAOXizCdS1es9QCyGurq6vMq6Ss6ykd87ByZb0xuKaRoS7/DFCU8//XTeZNcIYjGBfLkHWjj7lbtaKAr5Cn//5VvQfCZEjX+5x5fyuNwzkvLzQiZeM753u6Ap2S9eig+EEOkYgsvlypvygdNZvny5fr90+UfnZrwdOSLj2v/l99svv/xywdxvE0Jys2fPHjQ2NiIUYDh9KvOs8skkEgxv/3qsHFqhEeXNzp6dJmAz+nPR98aJKGBDioKYjIhpqSnsIoCTr5MV2br//vtRUVGBhBpCVE7f0yedqDyEhBpCZWVlwd2QHTp0CPPnz4eqJeAPdcxoGyOBdjBouOmmm/TgYKEQH+KhQCdUNTHNs8fEo0NIxP0oLS3N+/rUQllZGZ5//nkAgK/nA2g5ZtnEQgOI+q/D7XbjpZdeMmKIjrBo0SL9QinSl1mgjzGG8Ohz86UPSTbmzp2Lo0ePAgDCndn3zFJjYcT6+Kr3QgoKCydOnIDb7YYy0g81nHkN+Hgnn9i466678nJi48iRI1izZg2gKIid+8Cy/SpDA5CvX4PL5cInPvEJy/ZrNBFQUHozK7+ojj6v0LJrBLEaW2m/POOeJEo7z3I8duxY3r2nbr/9dsyfPx8sGod89pIl+1QuXQPzh1BXV5eXn11Lly7FTTfdBDCGxNmOrH8/fvYqwBhuvvlmnuFU4ETFBnk4dTGKPBhM+Xkh27ZtG5qbm6FGEgiezb707cjb/Fpm586daGpqMnp4tnnxxRfhdrsxdGYAwa6ZlYvr/q+rUKIKli9frl8zEkKI1+vVK1j8+v/mvsDr1OsxRMIM8+bNK8hrYlHZpbNTQzg8+fXwuXN8LmfTpk2WjGsmKGBDioJY8RRH6gV2XAul/LzQVVdX6xeAvljmpWd8sQ4AfGV3VVWVGUOzjdvtxqOPPgoAGAlk36tF01T4AnyiuRB6+4y3du1aLF26FIypCPkzf80EfHzS59ChQ6iurjZreJa799570dLSAlWOIjSYXdbReP4eXh7r+PHjBVeSUQR2w30XwZg27fPj/l6osRCqqqryqgxPNsR5JtbbAS2R3UrmSNcFAAxbtmzBypUrTRidvZqamnDLLbcAAOK9mfXgYIqMxABvIJ+vQeHkgEni8gXLsmxiZ3kvpDvvvDOvS3hu3rwZLpcLLBiAFpm+DJg6mmHj5JV0ubjttttQWloK5vNDGxrO+vdZPAH1aicA5GUpHo/Ho2dNy6fOgmnTf/bkgjEG+R1ezvDRRx9FRUWFqfszy5133gkAiJ/JriQlACTOdKRso9CJazVlKKg/pskKVF8YAIoiaOV2u/VskpF3OrP6XcYYRk7l7zlmKgsXLtSvXzv/I/tel5qqofMXPDvy6aefhstF03SEkDEf+chHIEkSzp+W0d+bWxbxr/8fv9944IEHCqIs5Xii3QVjwIXz6Y9VLMbQ0cEDNiJD3Ynok4AUhYULF46uFBy7edOYAgV80qwYVkQJosFzKN4NjU1/steYglCclzvIx9WDmbjnnntQWVmJhBxAOJrdarFg+BpUjff2ETXkC4kkSbj77rsBAEF/R0a/w5iG4Ghwp9Bu4ktKSvQJIX/vhxkFI9JJREYQ8XVCkqSC6kciiECdGg8jEZi+BFh0gN+kHj16tGBr4K9du5YvDmAaIt2ZB/uYpiF6/QIA4KGHHjJreLY7ePAgACDRk1nAJtF/HdBULFiwAKtWrTJzaKbavn071q1bB2ga4uez6/s0E8rIEJTe63C5XHrGYL6aNWsWbrjhBgBj2TOT0aIRaIEAJEnK+55qk6murtb7S8ykLJrScQ1QNSxbtkw/rvnmxIkTqK+vBwuGoVzM7FwyU2pXL7SBEZSXl+Phhx82dV9muu222+ByuaD2DkMdCU7/C6PU4SDUvhG43W7cdtttJo7QORYtWgQAYDFZf0z182BxVVVVwfUBmIzoZRo41wc1nvnEYbTbj8RQGGVlZbj11lvNGp5tnnjiCQBA75vXEfdntzCn/51uxH28xGuh9IolhBintbVVL//83/9v5gu8ujpkXL2kwOv1FlzlnGSi6s2F9vQVUS5dVKFpvCemU/vXABSwIUXC6/XqF9lCgvHVUA0NDairq7NhVPZYs2YNLxkBDZHEwLTPDycGwKBhwYIFeXsDP53q6mo9zdQXzC5rYiTIey489NBD8Hg8ho/NCcSNQzTcD0We/gIhEu6DpsbzrgZ+pu677z7U1dVBiYcQGcludaHg7+NlsQ4ePMgbZxeY0tJS7N69GwAQG+ma8rmMMcSGrwMADhw4YPrY7CQC5tHuzPuIxQe7oCViaGxs1CdjC9HBgwchSRKUkQGo0fC0z4/38KDwoUOH8jqzUZIkfOxjHwMAxC9fgBbPvo9ENuKjpdeOHj064booH4nMLHWgf8rnqQP8emfVqlWoqakxfVx2Edlm6uWrWWcMK5f5e+rYsWN5+54qKyvTsxnlU2dmXBouE/I7/HP83nvvzev7iIaGBj3rLHEu8yBX4hx/vWzdurVoAhUVFRWora1NeUwL8OviuXPn5u37JlurV6/m95KyiuC5zBe6+d/j14O7d+/O24y0qdx0001Yt24dmMrQ83p29wfXf8XfTw899BBKS0vNGB4hJM+JHmJv/CoGRZ7Z9c1//5zfZxw4cAANDQ2Gjc1p1q1bBwBov5B+UUF7O3/c6a0xKGBDisayZctSvpcZv8AuxMnSqUiSpEfnQ4m+aZ8fHn3O3r17C/pG5MEHHwQAhCLdkJXpJwsBIBYfRiw+DK/XW5AN24TW1lZe4xwMocD0N/Oh0Uycw4cPF2QQq7y8XM82Cw5k1yQdADRVRni4AwBw8uRJI4fmKGMBm+tTPk+J+KAmIigtLdUnXwvVkSNHUFZWBjXshxLIrI9YtCd/e0pko6mpSb+4TkxTFo2pKuQ+PvEjMnPy2a5du/iCCFVB4tJ50/ajBgOQr/Nj++yzz5q2HyuJ14w6NHUmnwjYiOcXqp07d2LWrFlgkSi03qmDWMlYJAqtm0+85vvq7oceegjl5eXQBkagdk1/nTsT6uAI1M4euFwuPP7446bsw0oiQyZxYeoFFsnEc4slu0aYM2dOyvdKMJr28UImSZJe/st/uifj3xPPLcSKBIK4P+h+/VrGAeNIXwi+i8NwuVy45557zBweISSP7dmzB83NzQgHGT54O5717yfiDG//mgdsCrVyjiCu9y9dUqFpE8/F7aOZN06/L6CADSka41eSyoynsBda74hMiIBNONE/5cUkY0wP2IjJ10K1bNmy0UbpDP5QR0a/4wvykiOHDx8u6BUKwNgN+XQBG8Y0hAL8Jv7IkSOmj8su4oYqGuiGHA9N8+xU4eEOME3BkiVLHN3kLle7du2CJEmQQ8NQE5NnZomAzpYtW1BWVmbV8GxRXV2tBxiiPdM3xdbkOOIDhVnvPZ1My6LJg91gqoLm5ubRYHJ+kyQJTz/9NAAgfvEcmJJbberJxC/wkmu33nprXveuSXbzzTcDADSfD0yWJ32eOsgDOk6/MctVaWmp/j5SLnVk/HtKxzWAMaxduxYLFy40aXTWqKur07Om5XfPmrIP+d1zAPj13/z5803Zh5X27dsHSZKg9g1DC07fD0oLRqD2j0CSJOzbt8+CETpHc3NzyvfqaMBm/OOFTvy/B8/1gqkZ9CocDCHeH4Tb7S7oe8ojR46gsrIS0YEIfO2ZLczpHs3G2bVrV1EF/ggh2fF4PPocxOs/z74s2ru/jSEWZZg/f/7ovFfhWrp0KSoqKhCPA729Ez+jrlzhAZu1a9daPbSsUMCGFI3xN6CJ0QybfL8xnYnNmzejpKQEihZFQp18sjmhBqFoMZSWlhZszfdkot9KIDR9KRHGNATD11J+r5CJ1XDRcD8UZfKSPdGkcmiF/JpZuHChXu4tNJhdGT2RlXPvvfcWdNZaQ0OD3lsk7p98lXPcx1d1b9++3ZJx2e3YsWMAgFjf9OeZ+EAnwDQsX74cbW1tVgzPVuI8Iw/1TVkaLLkcWqE05j18+DBaW1vBEnEkrmSfuTcdLRJG4ioPEn70ox81fPt2aW5uRktLC8AY1MH0ZV6ZokAb5hNnhR6wAcYyZJSOa2BaZn3WRDm0fM+uER577DEegLjaDW3Eb+i2tXAUyoUOAGM9K/JdY2OjHvxOXOqe9vmJi3yhxbp16wp+wdJ4EwI2geIM2Kxbtw51dXVQozLCHcPTPj9wll/rbdy4saDLUlZUVOiL3HrfmjrDHOCLI/tGnycCzYQQMhkRsDl/WsZQf/r+LJN5/Rf83uree+8tmPunybjdbv3e+WpH6nEaGdHg8zG4XC7H90Et7P8lQpJMzLCJpn28GJSXl+sr+8OJyUtmiJ9t3ry54Fe+A3zyr6SkBAk5gHjCN+Vzw9FeqFocDQ0NRTHR3NraOtrDiCEcmLwuc2j0Z/v37y/IcmjJxAVTcPASGMtsUiwR9SEeHoTH4ymKjAkRtIv709c4Z0xDPNCf8txCt23bNsyaNQtaIgrZN3XJolgfn0Qt5Gy1ZPPnz8fq1asBMCT605fmYUxDYrQcWiH1PPJ4PHjmmWcAALFzH0yZLTITsbPvAZqGTZs26Y04C4VYJaj2pj/PqAMDgKahubm5KBbpbNmyhfdUicUzKoumRSL68wrlXLNo0SK9qXni7TOGblt+9yygaVi3bp2e4VUI9u7dCwCQO6bvSSKeIzL2i0lTU1PK92qIT4AVW8DG7XZjx44dAIBg+/SlB4MX+DmmkLNrhKNHjwIABk71QJsm+yhwZQSx4SgqKyv19yAhhExm/vz5+tzTb3+ZeZZNX7eCy+dluFyuogkOr1mzBgDQMS5gI75fvHix4/upUcCGFI0FCxakfC8jmvbxYrFz504AQEROvyIVAMIJ/rNiCEgAvFyRuPkMRqau4x0M858Xap+WdMTkaDiUfvUlYwzhIF8lVsjN0YWDBw9i1qxZUBNhRAOZNV0NDvBsnL1796KxsdHM4TnCWMAm/c28HBoBU2VUV1cXRQYJAJSUlOjvDxGQSUdTEogP8fdaoUyiZkKfZO1Lfw5WfENgiTiqq6uxceNGK4dmunvuuQcLFy4Ei8cQbzeulJMa8CPRwbNrPvnJTxZcZp/IdlT6JgnYjD6+devWgvvb0/F6vfrntcicmYp6hS+0uPnmmzF37lxTx2al559/HgCgnLsMdXDEkG1qwTDk98+nbL9QbN26FQCgdPZNWy5Z6exP+Z1iMiHDZjRgM3v2bDuGYyvx/x9qn/xeEgCYqiF8mZelFOfrQnbLLbegoaEBcljGyPmp+6v1vcOv8/bt21cUiyMJIbkTvZPf+GUsbX+WdH77S/5ZtXv37qJZYCCyZzo7UwM2V6/y7/kiQWejgA0pGrNnz0Zpaan+PQOvEd/a2mrXkGwlgjCRxCA0NjGdUmMqojIvISKCO8VA1H4XAZl0GNMQivDARCE3zhxPrIqLhnrBtImvGTkRgCKH4fV6i+ImvqysTF9FFxqcvh8JYxrCQ7yBfLGsbNm8eTMkSYIS8UGNT1wFFPPxG9VNmzbB7XZbPTzbiMnU+EDnpBNjiaFugGlYvHgxli9fbuXwbCXOM/JAd9pyTiKQs2PHDni9XkvHZjav14tXX30VABC78CG0RPYNRdOJnX0PYAy33nor1q9fb8g2nURk2GjDw2DyxP4/Sh8PGBfD55IggrxKx+TnGEHpuJbyO4Xipptu0hujJ37zniHbTLzxPqBq2Lx5c8Fll6xZswbl5eVg0QTUAd+kz1MHfGCxBCorK/XVq8Vk/GIbLczP08UYsBHBl0jnCNTo5FmhkWsj0OIKamtr82KCLFcej0e/nxz8YPLsI8YYBt/nPxfnKkIImc6BAwdQXV2NkSENl85On5GvaQxv/RcP2BTLHAQAfTFoZ2fq/eS1q1rKz52MAjakaEiSNCE4U19fj6qqKptGZK+VK1eioaEBDCpism/Cz2PyMBhUzJ49u2CaE2fi1ltvhcfjQUL2IyEH0z4nGh+EqsVRU1NT0E3jx7vhhhtQX18PTZMRjU5spBkO8sn3zZs3Oz691CiirFnE1wlNnfqCKervgarEUFdXVxQlIQDe/PnGG28EAMT9PRN+Hh/hr5liCgoDPGBeWloKNRaCEvKlfU5sYKy8YDFZu3Yt6urqwBQZim/iql25nwfLC7V0yJEjR7By5UpAkQ3JslEDPsidHQCAj3/84zlvz4laWlp4ZghjUIdTeykwVdX71xRaKbipbNmyBZWVlUAsBm1o8v4SLCHr5dAK8Vzz2muvwe12Q+24DrV36pXu09F8ASjnrgAozEy1kpIS/T2iXJt8klm+yn+2cePGgguaZ2J8YEYbzbAphqzp8VpbW3lpcY0hdHHy8ovB8/w1s23btoLvmyCIgO7g6ckz1iJ9IUQHI/B6vUVTzYIQkrvS0lK95+Abv5q856fQfkaGf0RDTU1Nwd4/pbN8+XK43W6Ew6mPX7vGFx6vXLnShlFlpzg+MQkZNW/evJTv58+fb9NI7OdyufSVUSKTJllY5je2xVJCRJg1a5Z+wxqOpi+vEo7wx3fv3l1UN6sul0u/oYiFJ97MR8P8uIia1sXg5ptvxqJFi8A0BeGRa1M+NzSaXXPHHXcU1etGvB5ENo2gqbLev6bYAjYVFRX6+Tc+ODGbjzGG+CAPTIgSYcXC7XbrxyYxlHqe0RJxKH7+eVWokxsulwsvvvgiACDefjbnLJvY2fcB8OzRQl7ZLHqJqIOpQT7NNwJoGmpra4uif43g9Xr195HaOXkTebWnF2AMCxcuLMjjs3jxYtx5550AgMRbp3PaVuLtMwBj2Lt3L9atW2fA6JxHvGZEUCYd5WrxZawlG9/DBoxBkqSizLABxrJiA+cnf82In+3atcuSMTnB1q1bUVJSgthQFOHeUNrnDH041sOxsrLSyuERQvKcuLZ5/604FHnqTOp3XudBndtvvz2l4lChKy0tndjHXGbo6eEZNqJkmpNRwIYUlfG1uYu1HJogyohEEhNXHUZG+9cUQ63h8cQNRSgyMSMAAEJR/nixTTIDvC4zAETCqSvpGNMQHX1MvK6KgSRJuOOOOwAA4eEp+pFoCiI+njEhnl8sRMAm7kt9zSQC/QDT0NraWpAThdMRxyUxMjEwrIRGwOQ4KisrC6qpdabEOUQeSn3NyMN80mfx4sUFXX/54MGDepZN4tKFGW9HDQUhd/Hz0sc+9jGjhudIN910EwDo2TSCOjik/7yYFp8AY9cyatcUAZvRYE4hT6Q+88wzkCSJZ9nMsJeNFgxDOc8XXTz33HNGDs9R9IBNZ3/akpRM0yCP9q8p1KD5dGbPnj3hXNLY2IiSkhKbRmQv/Z7pfPoMGyWSQLSLv++K6b6poqJC7+M4fDZ9j5+hM/zxYsm6J4QYZ/369WhubkYsynDug8Skz1MVhvff4ou/Cq30bSbGZ9F0d2tgDKitrZ24AMOBKGBDisr4gM34jJtiI1bHxdTUG1iNKYgpPgDFNfkuiJuPSKxvQn8fRY0hnuDHq5gySQTxeohHU4N88ZgPmsabxxfyKu50xMVPNNANTUl/wRT1d4NpClpaWopuAn7t2rXweDxgSmrKdiLAX0Pr168vuolUYOz8mxjpA8ZNjCWGeVB406ZNRZWNJYjzjDquJJo82Jvy80Llcrnw1FNPAQDil86BqRN7hmUifvEswBh27txZ8OflG264AQCg+v0pj2u+4ZSfFxMxOar1D4Il0pfsVLv5uaaQr2eWLFmC2267DQAgn5pZmUH5vXOAxrBly5aC7AMltLW1obq6GkgoafvYqH0jgKygpqYmL0qJmMHr9U4of5YPkz5m2bhxIy8lPRKBPBKe8PPwpQGAAUuXLi3ohRbpiPNquoCNKqvwXRxKeR4hhGTK5XLpva/efWPybPwLZxKIhBgaGhr0IHIxGd/aofs6v6davnx5Xsw/UMCGFJXxF4pz5syxaSTOMH/+/LQ1l2OyHwBDc3NzUQa1Vq1ahcbGRjCmIhpLXa0bifIV3m1tbUVZ/mDhwoWoq6sDkJp6Gx/tabN27dqiah4P8A/8pUuXAkxDxD+xvBUAREbLpR06dCgvLg6MVFZWlnZiJxHkN7DFFsASli9fjoaGBkBTIftTb+ZFwKbQAxOTWbRoET+/jqv7rozw41QMvcNuv/12NDc3g8WiSHReyfr3tUQciSsXAUAP/hQy/RwzrlC1OuJL/XkRaW1t1Xv7aAMTM6m1SAQsEIIkSQX/nnryyScBAEr7VWjhaFa/yxIy5DOXABT+e8ntdusBKaVn4mtGuc7PwevXry+aXiTp8OvgMQ0NDTaNxH6VlZV6r8LQ5YmvmdAl/pjI0C8mIgttpH0ImpK6MMd/eQSarKGpqQnLly+3Y3iEkDwnAjan345DVdKXRXv/TR7MOXDgQNHN0QB80U4yUQ5t6dKldgwna8V7pUWKUn19/ZTfFxtJktJOlsYUviK1GEuIAPy4iIvsaCy1JrPoa1Osq6EkSUq7Ujse41lHhb6KOx1JknDgwAEAQMSXrh+Jpj9+8OBBS8fmFOPPM4wxJIL8Jl6UMio2kiSN9bEZTjrPaBrPukHxlpyRJAlr1qxJeYxpGpQAP8+IyaFCVlJSgpMnTwIAEhfPTdq0eDKJjkuAqmDlypVFUdq0rq5uwqIcxgDN7wNQnAEbgK9+BwC1d2K5Iq2XT76vXLkSs2bNsnRcVlu7di3vO6NpkE+3Z/W78rnLQELG4sWLC7p0nCD6OCrXJ06+y12DKc8pVuMDNsV+PylWbUeuTSw5GO7gC7qKMWCzcuVK1NbWQkuoCF0PpvzMf4nfa2/evLko77UJIblbv3496uvrEY0wtJ+dmEmtaQwfvM2rfxTrHMT4wExPL8+wGR/IcSoK2JCiUltbm/L9+AvuYrR27doJj8UUXlKkGCbFJiMmuCKx1JXvkRif9CjWZqtA+qBMPDo86c+KgWgMH/Vfn1D3PR4agKYmUFNTU9ClVKYyvhyRmohCU+Jwu9150fDPLCIgkxgZ65cl+wfBVAX19fVFO8kMpHnNhAOApqKysrJoeh7dc889KCkpgeobhjo8cfJ0MowxJC6fBwA88sgjRTMZtGzZspTvWSQCKAq8Xu+EpqPFQmTOpAvYqH38MRHUKXQiAKqcuZi2P0s6jDEoH/JMtUceeaQoskrWrVsHAFB6J06+qz1DKc8pVuPvH4v9flIsvIl0+VIe12QVsR5/ynOKicvl0hcs+TtS30/+y/y+qVjvCwghuXO73fqi0Q/emlgW7epFBUG/hurq6qKt2jD+nrG/j1//5ct9QeFfdRKSZPwKwpqaGptG4hxtbW0THouPBmyKdfIdGFsJFksM64/JchiyEobb7S7q1YXjJ1IZ0/QMm2LsEwDwG9H6+npoqox4JHViNeLnTZ13794Nj8djx/BsN/5cokR8APjqltLSUhtG5Awi8KsExkovJkbLo91yyy1FMTk4mfHnEsXPzzFtbW1Fc1zq6+tx++23AwDiowGYTCj9PdBCQVRVVeGOO+4wa3iOM361nBYMAAAWLFhQtOdeMRmoDQ5PyNLS+odSnlPoDhw4gLq6OrBwFGpnz/S/AEDrH4Y25ENJSQmOHz9u8gidYc2aNZAkCSwYSXlcC0agBSNwuVwTMiCLDZVESyUW/8V7AymPR7v9YCpDfX19UZbYBsbOr4GkgA1jDP4rPgAU/CSE5Gbfvn0AgDPvxSdc5515lwdxdu3ahZKSEsvH5gRlZWUpfeb6+/kxypfFf8Vxx0vIqOrq6im/L0bjV7drTEVCDaX9WTHRa78niST4RPyaNWtQWVlpx7AcYfxEaiIeAGN85Xu+rFYwmtvt1iffY4HUMnqxAJ8YKtbyVgDv15I8yS6HeCA0XcC4mMydO3fCRI8c5JOoxbgaNdn4mu5qmE8EjW8eWegefPBBAIDcdRVMTmT0O4krvOTTnXfeWVSfVYsXL075XgsE0j5eTJYuXYqysjJAlsGCYyV5mKZBGy6eEoMAUFpaqgddRE+a6chn+fMOHz5cNIu8qqur09Z2V3r55/by5cuL6rySzvjP7WIP2MyZMydtX9jI1bHrmWLJ9BxPXMsFu/z6Y9HBKJSojJKSkqK+1yaE5G7Lli3wer0YHtDQP1ruSzjzHr9v2LNnjw0jc47W1lb963icl95OfszJKGBDikpFRUXK91VVVTaNxDnmzJmTchwSKm/YW1NTkxKNLkbjVxAmElQqDuArEpLfS4mYDwCv1VwsK9/TEQGbaLBXf0xTZMTDQyk/L0alpaWYP3++/r0S4xOH40sYFRtJkiacT2Q/f72kK1dZTFpbW1OyItQQP//mS5NIo9x88838faKqSFy7Mu3ztXgMcncnAOC+++4ze3iOMn61nBYOpX28mHg8Hn1CUBsayxhmfj+gFleJQQA4ceIEAEC9ch0sPnUAlKkqlParKb9XLNL1t1R66LNJoJJoE6XL1At38HMOVSUAYoNR/bFgpw8AX7Tk9XrtGBYhpEBUVFTofcTOnx67rgkGNHR1KACAnTt32jI2pxgfnGlqasqbCh/FO7NGitL4yWQK2PAJw+SsiITCJziWLl1atKuhhPErueOjAZvxK7+LjcvlSpngkeWx10wxE2X0EuGxSbF4ZAgAS5uxVWySL5aUGH/NLFiwwK7hOMb4cnEswW/qiz37yOPxpJxnRIZNvjSJNIokSXrgJdFxcdrny9euAJqGNWvWFF1Z0+SgMACwED/P5MsqOrOIxSfa0FhJHnX069WrVxfVQou2tjZ+badpUC5em/K56tVuIJ5AU1NT0dV+TxeUEQGbYs/+BCaW2KaATfqATeQq9Wmpra2d8NkU7OLXM8VeWpAQYowdO3YAAC6dGQvYXDrHv25ra0NjY6Mt43KKlpaWlO/zqURn8VyhE5JGvkRWzZY80S6PlkMrtkmxdMYHZhIyZQUIKQGbOJWdAfgx4WUWx5oZJ8K0IlVIvjhSYzSRKqQLdDY3N1PJTqSeZ7QIP/8W43nm+PHjcLlcUEeGoIaCUz430cmzcO666y4rhuYo42/AtDDvwTF+sqzY6Bk2Iz79Mc3HF6AUY2BYlEVTLnRM+Tzx8zvuuANut9vkUTnL+MxPxhjUPh7ko0lmPgk/1ffFaPwCATWagOynBSjAxBLjkR7+OV7sx4UQYgxRxePyBVl/TARsim3BSTrjF81SwIYQkleSo86KFgOQXycys4yfSFVUPvlDAZvUidREgk++F2v/GsHlck2Y5IhH+epCCtikXixpMr+Jp4BN+oANBcy5lIl2xuDxeCaskioGDQ0N+g2X3NUx6fO0cAjq8CAkScLhw4ctGp1zlJaWYvbs2fr3bLQkWrFnN4psYW1krCE4Gw3eFFtPKAA4evQoAEC93gctEk37HCYrUDquA+ABm2Iz/nOJReNgsQQkSaJrYFBP1HTGL3KL9fGgxNy5c4v++Iw/NuE+/tlE7yVCiBHa2tpQXV2NeGzsscvnePCGAjZIuTcAkFdtHyhgQwhJOYkpWnzCY8UqXQCirq6u6JuLAqmlrESGTbEHbIA0fY/CfEWqqGFdzMafUzweD+rr620ajXOk6x9B7yVufEBv3rx5RbfSXbjtttsAAHLX1Umfk7jOSzxt2rQJzc3NlozLaVL+boXX7i72BSj6ZGF0LDih+fjndjEGbObNm6eX9VIvd6Z9jnq1G1BUzJ8/vyg/vysqKlKC4+owf720trairKzMrmE5xvgSaON7pBajurq6lLI78X7+min2MtLAxMBMfITPqhZ7KWlCiDHcbveE3nMDfbziRzGXpBTGz0Hk0zwnBWwIISkX2CLDJp8iz2YpKyubsJq7mJrzTiV5QpkxBZIk0bHBxMCMKofTPl6Mxp9TGhsbi6p3wmSqq6tRU1OT8lixl3ASxgdsijkj68CBA5AkCapvGFoknPY5cjeffD506JCVQ3OU8Z/ZtbW1qKystGk0zlBdXT0xM200s6RYJ1NFBppyKX3ARrl0TX9esfZzTC4/qQ7zbAmaYObGn1MoYMMlL+ZKDPGqBMVYxnS8dO+bhoYG6n1ECDFMuv5yCxcupPMMMGGxNQVsCHGo8vJyu4fgSMknLXU0w4YCNtz4SY5injBMNn5Cec6cOdQTChNreAP8NTO+QW0xGt/wMJ8ulsw2PgOg2DMChDlz5qR8X8ylrRoaGrBu3ToAgNwzcZJZS8ShDvUDAPbt22fp2JxkfGZRsWYajZeuzGJTU1PRlirav38/AEC93g+WkFN+xjQNyrWelOcVo+TJd83PSzjRwhwuOUDjdrtp8cmo5MVc8SF6zQjpglZU+pYQYqR0AZt0jxWj8Qsj86nCB11dkKJSrKvkppMcdVZZfMJjxWz8BGExTxgmG//BV4x9JdKZP38+PB5PymO0IpUbv8KHzjFjxk8q0/uJG39cxgdwis2tt94KAJB7rk/4mdLbDTCGlStXFvXCgvHnlfGB4mJFvbJSLVq0iC880TSo3f0pP9P6h4F4ArNmzSrqyY7kgI3qo8n3ZFQWLr3kz57EMM/iS34dFauKiooJ1zPFfP4lhBgvXYnbYs2iHs/r9aZ8n09ZRxSwIYSkjTLTZCo3PguAVuty44OfNMHMeTyeCdlHVA6CG59llE8XS2Ybn9FY7IEJoaqqKuX7Yj8uu3fvBgAoA31gqpbyM7mvGwCwa9cuy8flJOOvXehahqMV3qkkSdLfK2pXb8rP1NHsmm3btk1YgFFMkjM9tQAvb1XMweBkyZM/jDEbR+IsyfcCsp+/ZmihGzc+c5reS4QQI7W0tEyoJjS+fxbh8mkOggI2hJAJpaxmzZqFkpISm0bjLOODWTT5kx6V0BszfjUhNZDnxk985dPFktnS9fchExX7+XfVqlV8EYGqQBka0B9njEEZDdjs3LnTruE5wvjP7Hwqe2CmdAGbYv9s2rZtGwBMyLBRr/cBALZv3275mJwkeaJdC/K+WVSuk0tetETVG8YkB2y0mAKAAjbC+ONA7yVCiJFcLteELNhiXpgzlXwqVU8BG0LIBMU+KZaMJn8yQ8dlzPhsI7opS298Wb1iVltbq39dWVlJAfNJFPtrRpIkfZJZGRqbZNZCQbBYFKWlpdiwYYNdw3OE8T1Z8ummzEzjMz8ne6yYbNy4EQDARgL6Y0xVofYOAgA2b95sy7icIuVaJsb7/NDkO5nK+MUn1dXVqKystGk0zjL+3oDeS4QQo1FP1Mzk0+cSBWwIIRMU+6RYsvFZAJQVkB4dlzG0ii4zxdrsOp3kc25y8Iakos+msUlmNSlgI7Jt1q5dOyFjttiMP6/QeYabO3fuhEyAYi/J09DQMKFciDY4Aqgq6uvri35l6vj3TmVlJb2fyJSojPTkqL8aIcRsyXMQjY2N1G9tEi5X/oRB8mekhBDL0KTYmPHHgiZT06Ob+DHjb1ipXFx69JoZk3yeofPv5Og1Az2DRh0e0h9Th3lGwPr1620Zk5OMXzVHrxnO6/VOmDwt9oANANxwww0p32tDPv3xYi91Nf7vp+x7Mp3xCwbo+ncMVWwghJgtuddnsff9LBQUsCGETEAlRMaMPxY0mZpeRUWF3UNwjORVc263m14zk8indGSzJU8q0wTz5MY30yxGy5Yt4+dbTdUfU/3DAICbbrrJrmE5RlVVVcr3dJ4Zk3zzTtkS3MqVK1O+14b9aR8nlBEwGcaY3UNwLAryjUmuROB2u+n8SwgxXPLnNAWFCwMFbAghE4yf8Chm4wM2NGGYHh2XMckXSPX19UW/SncyNJE6JvlY0Pl3cnSe4Wn8q1atSnlM8/NJ5tWrV9sxJEcZ/xqh88yY8Z9NJF3AhvezWbFihR3DcTR6zaRHAZvJUbnkMeNL39K9ASHEaMmf0xQwLwwUsCGETEDZEmPGT57SBXZ69JoZQ+WtMkOvmTHJKy3puEyupKTE7iE4wsTADMOsWbOoXxYAj8eT8j0FQMckT57SRCo3oYfNCA9+Ll++3I7hOBqVBE6P7gsmR6+ZMcn3A1TJghBihuRrOzr/FgYK2BBCJqAJwzFer1f/Op8alFmNmtqNSb4RGz95SMbQeWZM8kV18jmHpKKJMW7p0qVpH6PjMxFl2IxJvpGnbAmupaUl9folFgcALFmyxKYRORdN/pBs0aKlMcn3BrT4hBBihuRFSrRgqTDQ7CMhZAKqqzuGSvBkho7TGApeZYZeM2OSjwVNupPppJtMpgnm9ChgMyZ5wpBWeHMulwuLFi1KeWzOnDn0ukmDXjPp0Wf25Og1Myb53oBeM4QQMyTP4ZWWlto4EmIUCtgQQiagiPwYuqjODF0UkGxRhs0Yt9tt9xBIHlm8ePGEx8ZPOhOOJt7HJF/b0XEZM76U4Pz5820aibPRYi6SLXrNjKHrPEKI2SgwXHgoYEMImYBu5Ml0ysvL8Zd/+Zf695TeT7JFQT5CZqa5uXlCucUFCxbYNBpno8DwmORrO1qYM2Z8wKa1tdWmkTgbvWbSY4zZPQTHovvJ9Og1QwgxA5ViLzwUsCGEoLy8HH/6p3+a8j0hU5EkKWXCnQI2Y+j9kxkK2KRHN/JkOm63Gy0tLSmP0SRzevTZNIYybNKbO3fulN8Tjl4z6dEq5slRwDw9es0QQsyQfG6h+8kx5eXlWL58ud3DmBEK2BBCIElSSjNR6sFBskUrOsYkXyy5XPQxK5SXl+OVV17Rv/d6vTaOxrnoRn5MeXk5brvtNruH4UiUFZAZej+NSb62o4UFY+bMmZPyPQVs0qN7A5Ites2kRxOphBBiHUmS8nZOJj9HTQgxXPJqd7rATo8mfiZHARsyHUmSUla70/uJTEeSJDq3TKKpqUn/2uPxoK6uzsbRkHyQHKShgM2Y8QGb8dlrhKPXTHo0+T45yqQmhBDiBPk670ABG0IIgNTV7rTynWQrX1ctmC1fLw4IIc42e/Zs/evGxkY6B5NpJS/GoYU5Y5KDn+m+JxyVF0yPrvMmR/eT6dFrhhBiNlpMUBjo7o4QAiD1RowusAkhxB50I5+Kjkd69fX1+tcNDQ02joTkC+o7l151dXXK98klgskYujcg2aLzDCGE2IPun1LlawCLAjaEEACpJa3opowQY+TrxQEhxNmSJ5VramrsGwjJG8nXdjSROmZ82SZ6P6VH9wbp0XXe5Og8QwghhMwcBWwIIQBSAzZut9vGkTgX3ZSlopUb06NjRLJF5xmSieRJZZpgJplInnCn3lCTo14t6VHAJj26zhtTXl6Ob3zjG/r3dD9JCCH2oPvJwkABG0IIgNQeJHSBnR7dlBFCCHGCyspK/evxJZ0ISSd5wp2uZ8h0ysvL8ZOf/ET/noJ86dGk2BhJkqjENiGEOABd5xUGCtgQQgCkBmkoYEOIMehGnhBihoqKCv1rygggmaBrO5INSZJSgsH0+kmPJsUml7wYsNjR5zQhhNgnXz+r6VOUEAIg9aI6X09oZqPJd5Itei+RbNF5hmQiefKHJoJIJmjCnWSLsu9JtpKveykra0zycaFAFiGEkEzQpwUhBEDqhSRNMqdHx4UQYjY6z5BMJDdKp8bOJBM0SUiyRdn306NFFpOj6xlCCLEHnX8LA125E0IA0MqfTNBNGckWvWZItug1QzKRHLChPgEkE7TaneSCJn/So+NCskXXeYQQs9F5pjDQrCwhBACVRCPEDPReItmi1wzJBDWQT6+8vBzLly+3exiORK8Tkgt6/RBiDHovEUIIyQQFbAghACjDJhN0gZ2Kjsf0aHULIbmh80x6yeWJ6BiNkSSJrmEmQa8Tkgu6nkmPjgshhBDibCdPngQA3HHHHTaPJDtFnRt/9epV/Nmf/Rl+8pOfoLOzE6WlpVi6dCnuu+8+vPDCC6ioqLB7iITYgm7q06ObslR0PKZH7yWSLXpfkUxQPwlCiJXoeiY9Oi4kW/SaIYQQa911111oamrCunXr7B5KVoo2YPOjH/0IDz/8MAKBgP5YJBLBW2+9hbfeegt/8zd/g5/85CdYtmyZjaMkxDp08Tg9OkaEELPReYZkIjlgQ0E+QgghhBBCCJnI4/Fg9+7ddg8ja0VZM+DUqVO4//77EQgEUFVVhf/5P/8n/vu//xv/7//9Pzz99NMAgAsXLuD2229HMBi0ebSEWI8mDAkxBk2kEkLMQCXRCCFWouuZ9Oi4kGzRa4YQYoby8nL96+RelyR/FWWGzcsvv4xoNAqPx4Of/exn2Lp1q/6zW2+9FcuXL8enPvUpXLhwAV/72tfwxS9+0b7BEkIIIQWCJpYJMUbye4kmfwghZqPP7/TouJBs0WuGEGKG5HPLggULbBwJMUrRZdi88cYb+NWvfgUAePLJJ1OCNcJrr72GtrY2AMCf/umfQpZlS8dIiN1o8ocQY1CfCUKI2Wjyh2QieeVlaWmpjSMh+YLOLYQQQkj++OY3v4nnnnsO+/bts3soxABFF7D5p3/6J/3rxx9/PO1zXC4XHn30UQCAz+fDz3/+cyuGRohj0A0aIbl56KGHAAAvvPCCzSNxFgoGk2zNnTvX7iE4EpU9INlKvraj6zySLfr8To+OC8kWvWYIIWbZt28fXnvtNbhcRTfVX5CK7n/xv/7rvwAAlZWV2LBhw6TPS25I9Otf/9r0cRFCnI8usFM1NDTYPQTH+tznPod/+7d/w/79++0eCskzdJ5J9cQTT2D79u346le/avdQHIUm3AkxRnLwk6RK/jyicw4hxqD3EiGEkEwUXQ+bs2fPAgCWLVsGj2fyP3/VqlUTfoeQYkEThunRBXaq1atX4xOf+ATmzZtn91Acx+v1YunSpXYPg5C8V1tbi7/927+1exiORp9NhMwcvX8IIYQQQojTFFXAJhaLYXBwEADQ2to65XPr6upQWVmJcDiMzs7OtM+Jx+OIx+P694FAwLjBEmIxWkU3ufnz56OzsxMbN260eyiO8+yzz9o9BJJH6NwyPUphJ9miRRYkW/SaIdmi10x6dF1DskXvJUIIIZkoqlmBYDCof11VVTXt8ysrKwEAoVAo7c+/9KUvoaamRv83f/58YwZKiM3oQjLVH/3RH2H37t34/d//fbuHQggpUMeOHQMAPPzwwzaPhOQb+swm2aJJZpIJ6ntECCGEEGKPogrYxGIx/euSkpJpn19aWgoAiEajaX/+2c9+Fn6/X/83WSYOcZaPfOQjAIBDhw7ZPBLnopuyVOvWrcO3vvUtLFu2zO6hEEIK1Je//GX86le/wpo1a+weCskz9JlNCDEDBWwmJ+4Jtm/fbvNICCGEEFKIiqokWllZmf51IpGY9vmi3NlkzShLS0v1oA7JH6+++ipaW1tx/Phxu4dCCCFFhTIBJuf1etHS0mL3MAjJe3SeIdkSpW8pKJGqvr4ehw8fhiRJqK2ttXs4jvLVr34V3/zmN/Haa6/ZPRRHWbx4MQDQHEkazc3N6Ovrw4YNG+weCiGEkDxQVAGb6upq/evJypwlC4fDADIrn0byR11dHZ555hm7h+E4yQHNTDLQCCEkW+vXrwcAzJ071+aREJL/VqxYgQsXLmDr1q12D8VRFixYgAsXLtg9DEdyuVzQNG3aXp7F5vOf/zxefvllKn07jiRJ+PM//3O7h+FIa9aswde//nW7h+E4ra2t+M53voP6+nq7h+I4X//61/H9738fH/vYx+weCiGEkDxQVAGbsrIyNDQ0YGhoCF1dXVM+d2RkRA/YUG8aUgyqq6vxh3/4hwDG+jcRQoiRNmzYgP/1v/4XlixZYvdQCMl73/rWt9Dd3Y22tja7h+Ion/rUp9DZ2YnnnnvO7qE4zg9+8AMMDw/Tvc04e/fuxalTp+B2u+0eCiF5b8eOHXYPwZHWr1+vL1wihBBCplNUARsAWL16NX71q1/h4sWLUBQFHk/6Q3Du3Dn9a7oRJsXirrvusnsIhJACd/DgQbuHQEhBaGlpoTJ6aSxZsgQ/+clP7B6GI9100012D8GxKFhDCCGEEEKcwmX3AKwmVnyEw2G8/fbbkz7vP//zP/WvqZkgIYQQQgghhBBCCCGEEELMVHQBmzvvvFP/+tvf/nba52iahu9+97sAgNraWuzdu9eKoRFCCCGEEEIIIYQQQgghpEgVXcBm8+bN2LlzJwBe+/v111+f8Jyvfe1rOHv2LADg5ZdfhtfrtXSMhBBCCCGEEEIIIYQQQggpLhJjjNk9CKudOnUK27dvRzQaRVVVFX7nd34He/fuRTQaxfe+9z381V/9FQBgxYoVeOutt1BdXZ3RdgOBAGpqauD3+zFr1iwz/wRCCCGEEEIIIYQQQgghhDhcNnGDogzYAMCPfvQjPPzwwwgEAml/vmLFCvzkJz/BsmXLMt4mBWwIIYQQQgghhBBCCCGEECJkEzcoupJowh133IH3338fr7zyClasWIGKigrU1tZi48aN+PKXv4xTp05lFawhhBBCCCGEEEIIIYQQQgiZqaLNsDEDZdgQQgghhBBCCCGEEEIIIUSgDBtCCCGEEEIIIYQQQgghhJA8QgEbQgghhBBCCCGEEEIIIYQQm1HAhhBCCCGEEEIIIYQQQgghxGYUsCGEEEIIIYQQQgghhBBCCLEZBWwIIYQQQgghhBBCCCGEEEJsRgEbQgghhBBCCCGEEEIIIYQQm1HAhhBCCCGEEEIIIYQQQgghxGYUsCGEEEIIIYQQQgghhBBCCLEZBWwIIYQQQgghhBBCCCGEEEJsRgEbQgghhBBCCCGEEEIIIYQQm1HAhhBCCCGEEEIIIYQQQgghxGYUsCGEEEIIIYQQQgghhBBCCLEZBWwIIYQQQgghhBBCCCGEEEJsRgEbQgghhBBCCCGEEEIIIYQQm1HAhhBCCCGEEEIIIYQQQgghxGYUsCGEEEIIIYQQQgghhBBCCLEZBWwIIYQQQgghhBBCCCGEEEJs5rF7AIWEMQYACAQCNo+EEEIIIYQQQgghhBBCCCF2E/ECET+YCgVsDBQMBgEA8+fPt3kkhBBCCCGEEEIIIYQQQghximAwiJqamimfI7FMwjokI5qmobu7G9XV1ZAkye7hIBAIYP78+ejs7MSsWbPsHo5j0HFJj47L5OjYpEfHZXJ0bNKj4zI5Ojbp0XGZHB2b9Oi4TI6OTXp0XNKj4zI5Ojbp0XGZHB2b9Oi4TI6OTXp0XCZHxyY9Jx0XxhiCwSDmzp0Ll2vqLjWUYWMgl8uF1tZWu4cxwaxZs2x/UToRHZf06LhMjo5NenRcJkfHJj06LpOjY5MeHZfJ0bFJj47L5OjYpEfHJT06LpOjY5MeHZfJ0bFJj47L5OjYpEfHZXJ0bNJzynGZLrNGmDqcQwghhBBCCCGEEEIIIYQQQkxHARtCCCGEEEIIIYQQQgghhBCbUcCmgJWWluILX/gCSktL7R6Ko9BxSY+Oy+To2KRHx2VydGzSo+MyOTo26dFxmRwdm/TouEyOjk16dFzSo+MyOTo26dFxmRwdm/TouEyOjk16dFwmR8cmvXw9LhJjjNk9CEIIIYQQQgghhBBCCCGEkGJGGTaEEEIIIYQQQgghhBBCCCE2o4ANIYQQQgghhBBCCCGEEEKIzShgQwghhBBCCCGEEEIIIYQQYjMK2BBCCCGEEEIIIYQQQgghhNiMAjaEEEIIIYQQQgghhBBCCCE2o4BNgZAkKaN/e/bssXuoGevv78ePf/xjfP7zn8eRI0fQ2Nio/x2PPfZYTtuORCJYsmSJvr1FixZN+twvfvGLGR9f8e+LX/xiTuObihHH5Tvf+U7Gf8t3vvOdabcXiUTwla98BZs2bUJ9fT0qKyuxatUqvPbaa7h69Wpuf3AWnHRsQqEQfvnLX+KrX/0q7rvvPixevDij15sZnHRcOjo68PWvfx0nTpzA8uXLUVFRgbKyMrS2tuLOO+/E9773PSiKYswfPg0nHZef/OQn+OIXv4jbb78dbW1taGxshNfrRV1dHTZs2IDXXnsN58+fN+YPz4CTjs1k/r//7/+z7LwrOOm47NmzJ+PtWMFJxyZZOBzGX/zFX2Dfvn2YN28eSktL0dzcjPXr1+PFF1/Ez372s5n/0RnI9bh0dHRkfQ2SyWeM3ccFcM6xKbTrPDNeMz/96U/xwAMPYMmSJfrn9vz583H8+HH8n//zf6BpmjF//DSceGx+/OMf45577kFraytKS0vR2NiILVu24Ktf/SrC4bAxf/g0jLxn6ujowKc//Wls2LABtbW18Hq9qK+vx7Zt2/AHf/AH6O/vn/L3nXT9Czjr2BTaNbCQ63Fx0jWwkcflypUreOWVV7BmzRpUV1ejsrISy5cvx/PPP48PP/xw2t+Px+P4zW9+g69//et45JFHsHLlSrhcLkuv7YRAIIDvfe97eO2117B7924sW7YMNTU1KCkpQVNTE/bs2YOvfOUrGBoaynrbmqZh69atGV+30nFJr6+vD3/zN3+DBx98EKtXr0ZVVRVKSkrQ0tKCw4cP46/+6q8QjUZn+qdmzUnHZjLvv/8+vF7vjN/jZvj0pz+d8nf94he/SPu8QpybmYrVxyWZrfdMjBQEABn92717t91DzdhUf8fJkydz2vZrr72Wsr2FCxdO+twvfOELGR9f8e/v/u7vchrfVIw4Lt/+9rcz/lu+/e1vT7mt9vZ2tnz58kl/f9asWexHP/pR7n94Bpx0bPbs2TPp7031ejODU47L5z73OSZJ0rS/v2nTJnb16lXjDsAknHJcZFnO6Pe9Xi/70pe+ZNwBmIJTjs1kQqEQW7hwYco2vvCFL2T9d2bLScdl9+7dGW/HCk46NsJ//Md/THidjP9300035fR3TyfX43LlypWMj4n4d/DgwSm36YTjwphzjk2hXecZ+ZqJxWLsxIkT0/7+zp072cjIiLEHIg0nHZtAIMDuuOOOKX932bJl7MyZMwYfhYlyPS7Cd7/7XVZeXj7l9urr69nPfvazSbfhpOtfxpxzbArxGpix3I+L066BjTou3/zmN1lJScmk2yopKWFf//rXp9zGY489NuV4rPTv//7vGf0/NTY2sp/+9KdZbfvrX/96Vn8bHZeJ/uqv/oq53e5px7F8+XL23nvv5fpnZ8Qpx2YyqqqyzZs3z/g9boZTp04xj8eTMqaf//znaZ9biHMzk7H6uCSz+57JA1JQnnvuOTz//POT/ryystLC0RhnwYIFWLVqlSHRy1OnTuFP/uRPUFZWBq/Xi2AwOOXzn3/+edxzzz1TPkdVVezatQuBQACzZs3CnXfemfM4M2HEcfm3f/s3zJ07d9Kft7a2TvqzYDCI22+/He3t7QCAp59+Gg888ADKy8vx85//HF/60pcQCARw//3349e//jVuvvnmGY8zW3YfG8aY/nV9fT02btyI//7v/0YoFJrxeIxg53Hp6ekBYwyVlZW46667sG/fPixfvhxlZWU4e/Ys/uzP/gxvvvkm3nzzTezfvx/vvPMOqqqqZjzObNj9eqmpqcGePXtwyy23YMmSJWhpaUFFRQW6u7vxi1/8Av/7f/9v+P1+fPazn0VtbS2effbZGY8zW3Yfm3R+7/d+D1evXkVTU9O0KzfN4pTjsnHjRnz729+e8RjM4IRj83//7//FHXfcgVgspr9n9uzZg6amJkQiEZw9exY//vGP0dfXN+MxZmsmx2XevHn44IMPpn3el770Jfzd3/0dAODkyZOTPs+JxwWw99gU2nWeka+Zl156CX//938PAGhqasKnPvUprF+/Hl6vFx988AG+/OUv4+rVq/jVr36FBx54AD/96U8zHmeu7Dw2jDHcd999+t+7YcMGvPLKK1i1ahWCwSB+8pOf4Otf/zouXryII0eO4K233kJjY2PG48zFTM+/v/71r/HYY49B0zS4XC6cPHkSx48fx9y5c3Ht2jX87d/+LX70ox9heHgYx48fx+nTp7FkyZIJ23Hq9S9g77EpxGtgo14zTr0Gnulx+d73vodnnnkGAP/bXnvtNdx6660oLS3FqVOn8JWvfAUXL17ESy+9hKamJtx3331pt5P8Xqqursb69etx/vx59Pb2zvyPysH8+fOxd+9ebNiwAfPnz0dLSws0TUNXVxd++MMf4h/+4R8wODiIY8eO4Y033sBNN9007TavX7+O3/md34EkSWhoaMDg4OC0v0PHZaK+vj6oqoqSkhIcPXoUBw8eRFtbG6qrq3Hp0iX89V//NX72s5+hvb1dP79ke+81E044NpP58z//c7zxxhu23ksm0zQNH/3oR6EoStZjKsS5GcGO4yI44p7JtFAQsRRGo3tWrDC2yuc//3n2ox/9iPX29jLGUlfFzTT6rSgK27BhAwPA/uAP/kCPlua64utf//Vf9bE9+eSTOW1rOkYcl+TI85UrV2Y8lt/7vd/Tt/OVr3xlws9//etf69FwK7K7nHRsvvnNb7K/+7u/Y+3t7fpjRr3esuWU4/KpT32KffnLX2aBQCDtzxVFYffdd5++n9///d+f0X4y5ZTjwhj/26dy+fJlVldXxwCw2bNnT/v8XDnp2Iz31ltvMbfbzUpLS9lf//VfW/r556TjIjJsnJI566Rj09/fzxoaGhgAdvPNN+tjSicej894P5kw41pmPEVR2Ny5cxkAVl1dzSKRSNrnOem4MOasYzOdfLvOm04mx6W3t5e5XC4GgNXV1bHOzs4Jz/H7/WzRokX6+N58801DxjcZpxybH/zgB/p+Dxw4kPb98u///u/6iucXXnjBkLFNxojjcvvtt+u/8xd/8Rdpn/Pqq6/qz5nsb3LS9S9jzjk2hXgNbNRrxknXwLkel3A4zJqamhgAVlVVxT744IMJz/H7/ezGG29kAFhzczMLBoNpt/W9732Pffvb32anT59mqqoyxlKzq62UyTH/x3/8R31sd911V0bbvfPOOxkA9sQTT2T8t9FxmeiP/uiP2Kc//WnW398/6XOS34uPP/54RuPIhVOOTTqdnZ2surqaSZLE/vZv/9bw64iZ+OM//mMGgK1atYp99rOf1ceUSSZJoczNpGPHcWHMOfdMFLApEFZOWNnFiJuyr33tawwAW7lyJYvH44bdQDzwwAP62P7zP/8zp21ly65JsUQiwWpqahgA1tbWpl8wjffMM8/o+3rjjTdmtK+ZctIkM2P23rAmc9pxSTY4OKiXELjxxhtN2086Tj4ujKW+l06fPm3qvsZzyrFRFIWtW7dOv2j8+c9/buvnHwVsJmfnsXnyyScZAFZRUcE6OjpmvB0zmDHB/NOf/jSjm3AnHxfG7D0208m367zpZHJc/vmf/1l/zquvvjrptv70T/9Uf96f/dmfGTK+TNl1bJInqi9cuDDpth566CEG8HJOQ0NDhowvEzM5LmJCvKGhYdLn+Hw+fbvr16/PeDxOuf5lzHnHJlm+XQNbdVwYs+8aONvjkhzM/d3f/d1Jn5dcLmq60mjJ7ApMZGrlypUM4GWupvP3f//3+nMHBwdz+tvouEwvHo+zlpYWBoDV1NRMOndjNTuOzbFjx/TPeDOuI7J19epVVlVVxQCwX/ziFymles0OTEzHzs8lO4+LU+6ZXCCkSFy9ehWf//znAQDf+MY3UFJSYsh2A4EA/vmf/xkAsHjxYuzcudOQ7Trdz3/+c/j9fgC8XITLlf50kty87R//8R+tGBrJYw0NDVi7di0A4NKlSzaPxlmqq6v1r2OxmI0jsc8f//Ef49SpU1ixYgU+/elP2z0c4kAjIyN6KaOHH34YCxcutHlE5vvud7+rfz1ZaatiPC5AZsdmOoV4nZfJcUkkEvrX6coYCUuXLk37O/kqk2Pz1ltvAQCWLVuG5cuXT7qtw4cPAwBkWca//Mu/GDhK44n/u8WLF0/6nJqaGr20WyH8X2fKqmOTb9fAVr5m8uUaWJwbAODIkSOTPm/Pnj0oKysDAPzwhz80fVxWEf9P0/0fBQIBvPjiiwCAP/zDP0RDQ4PpY7OTE45LSUkJtm/fDgDw+/0YGhoybNu5sPrY/PCHP8S//Mu/oKGhAX/4h384o20Y7YUXXkAoFMLJkyexe/duu4eTws7PJbuOi5PumShgQ4rG888/j3A4jEceeQR79uwxbLs/+MEPEI1GAQCPPPIIJEkybNtO9l//9V/611OdQDdu3IiKigoAvNYxIdOJx+MAALfbbfNInCMajeoThi6XCytWrLB5RNbr6OjAF77wBQDAX/7lX6K0tNTmEREn+vGPf6x/Jh87dkx/PBKJ4OLFi+jt7U2pf57vgsEg/umf/gkAsGjRIuzatSvt84rtuACZH5vpFNp1XqbHZeXKlfrXly9fnnR7yTfwyb+TjzI9NmKiq7m5ecrtJf/8l7/8pTGDNIn4v7ty5cqkzwkEAnq/gHz/v86Glccmn66BrTou+XQNnDwJPtX5wePxoL6+HgDw+uuvQ1EU08dmtvPnz+Pdd98FAKxatWrK537mM59Bd3c3du3albK4sxA56biI8wvgjHOM1cfG7/fjpZdeAgB85StfcUSg8Pvf/z5+/OMfo76+Hl/96lftHk5adnwu2XlcnHTPRAGbAvODH/wAq1evRkVFBaqrq7F8+XKcPHkSP//5z+0emq2+973v4V//9V9RV1eHr33ta4ZuO3kl3qOPPmrotq3w+OOPY+7cuSgpKUFjYyO2bNmCz33uc7h+/fqUv3fmzBn966k+YD0eD5YtWwYAOHv2rDGDtshMj02hM/O49Pf366+Ttra2nLdnJaOPiyzLuHbtGr73ve9h27ZtaG9vBwA88cQTKSsN84ERx+a5555DJBLBQw89hFtvvdXE0VrHiONy7tw53HLLLaitrUVZWRlaW1tx/PhxfPe734UsyyaO3lwzPTa/+c1v9K9vvPFGvPnmmzh48KB+TdTS0oLm5mZ87GMfM7dJpEV++MMfIhKJAJg6mFBsxwXI/NhMJ9+v88bL9LjceOON2LZtGwDgO9/5Drq7uyc8JxgM4k/+5E8A8CycgwcPmjNoi2R6bETTXZFpPpnknydfNzuRaOQ+NDSEb3zjG2mf8z/+x/+Y8PxiYNWxybdrYDOPS75eAyc35J7q/MAYQyAQAMAzjy5evGj62MwQiUTQ3t6OP/qjP8Lu3bv1wNPHP/7xSX/n9ddfxze+8Q14vV785V/+pUUjtZYTj4ssy3j99dcB8GCiCBhazc5j8+lPfxo9PT3/f3t3Hldllf8B/HNZRNwAF9BcEDEw0dRcCDfcSsV9NHQ0AftZ6YxkuY5aLtVkbqOOYyrpgJqTpk6YOFlmqGhILmQqai5QmoCCIi5sl3t+fzD3GZC7c5fnXj7v1+u+fLjPued+ny8P1/Pc85xz0KtXL0yaNMnkeswlLy8P06dPBwAsW7ZMGo1oLEf7bsbWeZHVNZPNJmMjs8J/5+nT9Rg5cqTIy8uzdagmM3V+yXv37gkfHx8BQGzatKnCvqrOqZyeni4UCoUAIHr06GFSHVVV1XUCtD1q1qwpNm7cqLWO4OBgAUDUrl1b7/uVn+u7sLDQ0EOrMlvlRhu5zOEtt7yUN2vWLKm+lStXVqkuY8khL+Vj0PQYOHCgePDgQRWO0jS2zs2OHTsEAOHp6Smys7Ol5+19DZuq5KX8vM3aHm3bthVpaWlmOFLj2So3ffr0kcrGxcUJFxcXrXU1btxY/PTTT2Y6YsOYe67s8sdbfpFvXeXkmBchbJcbfTHZYztPF2PycuXKFeHn5yeAssWxV61aJRITE0VSUpLYsGGDtK9hw4YiOTm5yrEZy1a5CQkJEQCEs7OzzkWeo6OjpfqaNGlS5fgMZUpelEqliIiIEACEk5OTmDx5svjqq6/EqVOnxN69e6WFngHda3NoIpf2rxDyy0159tYGNnde5NgGNjYvmzZtksqvWrVKa7kzZ85UOLZvvvnGoHjksFaLvrbaX/7yF6FSqTS+tri4WAQFBQkAYt68eRX22fsaNnLMS3n/+Mc/pHqmTZtWpbqMJYfcHD9+XCgUCuHq6lphHSxbrmHz+uuvS+3L8sdv7Fotpl4zGcIW/y/ZOi9yumZyATmEWrVqYfjw4ejfvz/atGmDOnXq4O7duzh69Cg2btyI3NxcxMfHY8SIETh06BBcXV1tHbLVzJ49G9nZ2QgJCcHrr79u1rq3b98uDYczdW50W2nVqhX+8Ic/ICQkBM2bNwdQNuXF3r17sWfPHhQWFmLKlClQKBR44403Kr3+4cOHACreSaRN7dq1pe1Hjx7JfiqjqubGUVk6LykpKdKdus2aNcPUqVPNGb7FWON8adiwIdavX4/Ro0fLYgi7ocyRm3v37uGdd94BACxduhTe3t5Wi99SzJEXJycn9O/fH2FhYejQoQMaNGiAhw8f4uzZs9i0aRMuXbqEtLQ09O3bFz/++CNatGhhzUM0WVVzc+/ePWlbXe7DDz9EREQEfHx8cO3aNaxYsQJxcXHIysrCyJEjce7cOdSrV89qx2guv/32G44ePQoA6N69uzSaVZPqlBfAuNzoYs/tPE2MzUtAQABOnTqFDRs2YNmyZZg5c2aF/a6urpg1axamT5+OZs2aWSxuazAmN8OHD0dycjJKS0vx7rvvYtOmTZXKXL16FbGxsdLP6nazXDk7O2Pr1q0YNmwYPvroI2zevBmbN2+uUKZv376YP38+BgwYYKMobcMaubHHNrC1zhl7agMPHjwYLi4uUCqV+Nvf/oaIiIhKd4erVCosWLCgwnNy/3wwRMeOHRETE4OuXbtqLbNs2TJcvHgRfn5+eO+996wYne3IIS83btyQzrk6depg3rx5Zn8PU1grN8XFxXjjjTcghMCMGTMQFBRkashmk5SUhM2bN8PFxQUbN240aRS4I343I4e8yOqayWJdQWRV9+/f17ovKytLdOrUSeoFXLt2rfUCMyNTer+PHj0qFAqFcHFxEefOnau0v6p3fAUEBEi9tLYavWRKXvLy8rTexSCEEPv37xeurq4CgKhVq5bIzMysVKZVq1YCgGjevLne95s4caIU482bNw2K0RxslRtt5HKHodzyIkTZ51SzZs0EAKFQKMThw4eNer05yCEvxcXF4vz58+L8+fMiNTVVJCQkiGnTpgl3d3fh5eUlPvroI2MOyWxsmZtJkyYJACI4OFiUlpZW2GePI2zMlRdd/+8XFxeLyMhIKbZRo0YZFJs52So3/v7+Fe582rZtm8a63njjDanMxx9/bFB85mDOO/n++te/SnXpu4NO7nkRwna50cVe23namJKX2NhY0aZNG613F7Zo0UIsW7ZM59+updgqN3l5eaJp06ZS+VdffVWcO3dOFBUViZycHLFt2zbRuHFj6W5eoGw0jrWYmpe0tDQxbNgwrXeT1qxZU4wbN07cunXLqHjk0v4VQn65EcJ+28BCmDcvcmwDm5KXadOmSa8JCAgQ8fHx4sGDB6KgoEAkJyeLgQMHCgCiRo0aUrnt27cbVLccRpLcv39f+j39+OOP4vPPPxejRo0SAIS/v7/Yv3+/xtf98ssvombNmgKAOHDgQKX99j7CRo55EUKIx48fiw4dOkh1xMbGmlRPVdg6N0uWLBEARMuWLcXjx48r7LPFCJuioiKpXTV79uxK+w0ZSeKI383IJS9yumZih001cf36denEbN26ta3DMYmxH6aFhYUiMDBQABAzZ87UWKYqFxDJyclSPGPHjjX69eZiqf9kPvjgA6neDz/8sNL+tm3bCqBsigx9wsPDpbpycnLMFqM+tsqNNnK5YJVbXvLz80Xnzp2l1y5btsxsMRlDbnkp79y5c6J+/foCgJg0aZLZYjOUrXKj7pBxdnYWqampWvcD9tNhYwhznDMlJSXS/4EATPoSqSpslRv1tAkAxPPPP6+1npycHOHm5iYAiE6dOpktPn3MmRf1RY2bm5vODjwh5J8XIWyXG20csZ1nbF5mzJghvffIkSPFiRMnxKNHj0RBQYE4e/as1KEOQPzhD38QSqWySvEZy5a5OX36tPD29tb4JbX6sXTpUtGoUSMBlE3paS2m5OXYsWPCw8NDaqdu375dZGVlieLiYnHz5k2xfv16qR3yzDPPVJhSRh+5tH+FkF9u7LkNbMm8lGfLNrApeSksLBRhYWE6Pxu6dOkipk6dKv0cHx9vUN1y6JjQZtu2bUKhUAgnJyeNnQJ9+/YVAMTo0aM1vt7eO2y0sWVeSkpKKpyLU6dONboOS7JGbi5fviy1azV1DNmiw0bd8dCiRQvx6NEjrfsB7R0ThrC372bkkhc5XTPJ7xONLKb8h/Xvv/9u63CMZuyH6XvvvSeAshEgmv7ghajaBUT5RpamHn9rsdR/MtnZ2dK87S+99FKl/Y66ho0h9OVGG7lcsMopLwUFBVJjDICYNWuW2eIxlpzyosknn3wixWfoXNfmYovcFBYWSne3v/POOxpf76gdNuY6Z5YvXy7Ft2PHDrPFZwhb5ebFF180+POkR48eAiibe7+oqMhsMepirrykpKRI9bzyyit6y8s9L0LYLjfaOFo7z9i8JCQkSOWjoqK0llO3twGIv//97ybHZwpbnzO3b98W06ZNk9bKVD+6du0qEhIShBBCujvYz8/P5PiMZcpNbuoRQ40bN9Z6J+6FCxek4+ncubPB8cil/SuEvHJjz21gS58zT7NVG9jUz5jS0lLx6aefio4dO0ptFgDC29tbLFiwQBQUFIgpU6ZIzx89etSgeuXcMSHE/27SrF27tsjNzZWe37JliwAg6tatq/UGIkftsBHCNnlRqVRiwoQJ0mvDw8MrzVQgB5bMjUqlkspom23A2h02ly5dkkbX7du3T2MZc3VM2NN3M3LKi5yumbiGTTXStm1b/Oc//wEA/P7773jmmWdsHJFlLVu2DAAwYMAA7N+/X2OZx48fS//u3LkTAODt7Y1+/frprLu4uBi7du0CAPj4+GDgwIHmCls2vL290aBBA+Tk5OD333+vtL9Zs2ZISUnB48ePkZeXB09PT6113bx5EwDQqFEj2a9fYwh9uamujM2LUqlEeHg4EhMTAQCTJ0/GihUrLB2m1ZnrfBkxYgT+9Kc/AQD27NmDl19+2Vwh2oyu3Pz73//GL7/8AldXV7Rt21b6jC4vLS1N2r5w4YJUJjg4GH5+fpYN3oLMdc60bdtW2naUzyp9uWnevDlOnjwpbeui3q9SqXDv3j00btzY/AFbyLZt26TtiIgIveWrS14A43OjiSO284zNi3otCvXc3drMnz8fq1evxqNHj/DPf/4T0dHRVQ/Wykw9Z5o0aYJ169Zh3bp1yMrKQn5+Pnx8fODh4QEAuHXrFgoLCwFAFnPma3Pw4EHp8zQ6Olrr33xQUBBeffVVbN68GWfOnMG5c+fQoUMHa4ZqdZbKjb23ga19zthbG9jJyQmTJ0/G5MmT8fDhQ2RnZ6NWrVpo3LgxnJycAJStc6VWvr1mz0aMGIEvvvgCjx8/xsGDBzF+/HgA//teJjQ0FElJSRpfe+fOHWlb3Z6vXbs2hg0bZuGoLc8Wefnzn/+MHTt2AChbW+mzzz6Tzj05sWRuTp48WWFtOk3Xknfv3pW209PTpTLt2rVDu3btqnp4laxevRrFxcVo1aoVnjx5ojGmCxcuSNvff/89srKyAADDhg2rsDa0Pvb03Yyc8iKnayZ22FQjpizYZM+Ki4sBALGxsRUW/dQkJycHf/zjHwGU/aegr8MmISFBWoxqwoQJsl8A0VS6zpm2bdti7969AIDLly/jxRdf1FhOqVTi+vXrAIDnnnvO/EHaSHX7ezKUoXlRqVSYOHGi1Jk6duxYjYv2OgpznC+NGjWStn/99dcq1ycX2nJTVFQEACgpKcHrr7+ut569e/dKn0mxsbF23WEDmOeccdTPKV3HFRQUhN27dwMASktLddZTfr+Li/00iUtKSircZDJo0CC9r6kOeQFMy40mjtbOMyUvly5dkso3bdpUa7maNWsiKCgIKSkpuHz5snkCtiJznTONGzeudKF+5swZabtbt26mB2lh6t81ALzwwgs6y3bu3FnqzLt8+bLDd9hYIjeO0Aa29jljz23gunXrom7duhWeKy0txU8//QSgbIHshg0b2iAy89P2e1K36RMSEpCQkKC3HvX3Mr6+vg7RYWPtvMydOxcbNmwAAPTu3Rt79+6Fq6urSbFbmiVzo64DAGbPnq23jmPHjuHYsWMAgEWLFlmkw0Yd040bN6SYdfnggw+k7fT0dKM6JgD7+W5GTnmR0zWT/LpYyWLK34ns6KNrLM0cd2/K3d27d5GTkwNA8/nSs2dPaVt954Imp0+flkYy9ejRw8xR2oa+3FRXxuTlzTfflL4gGTZsmGzv+jEHc50v5e8AqVOnTpXjkgP+LWlmrrw44v/7+nLTu3dvafvGjRs661LfTFCzZk3Ur1/fjFFa1oEDB5CbmwsAGD9+vEEXCNUhL4BpudHE0dp5puRFXUapVOotW1JSUuE19sRc54wm6gt+oOzLD7kqf8z6ft/q3/XTr3NUlsiNI7SBrX3OOFobODExUfrckfNng7Ec7fdkLtbMy4cffojly5cDALp27YqEhAS4u7tb9D2rgueM5fC7Gc3s6VrSMX8DVEl6ejoOHToEAPD399d5p5yjEGVrNOl8+Pr6AijriVc/d+TIEZ315ubmSlPLdejQwWHvLIuJiYEQAkDZqKOn9enTR5ryYevWrVLZp8XFxUnbo0aNMn+gNqAvN9WVoXmZMWOGdKdd//79sXv3boe+6DfX+VL+i5/27dtXOS450JWbqKgovZ/h6iHbQNmdUOrno6KirHkYZmeOc0apVOKf//yn9HP5xqc905eb3r17S3fr7d+/X+udUenp6dLdrT169LCri5LynQmRkZEGvaY65AUwLTdPc8R2nil5UY9SzM3NrXA3/dPu3bsnTVNhjyMbzXHOaJKWliZNqzdgwAAEBASYrW5zK/970zb1jFr5m7Ts8fdtLHPnxlHawNY+ZxypDSyEwOLFiwEArq6uBo0itxfafk8ZGRl62/Tl23Tq5zIyMqwZvsVYKy9r167Fe++9J73PwYMHK43ukhtL5qZPnz5660hPT5fKR0ZGSs+r/0bNLS4uTm9MixYtksonJiZKz7ds2dKo97Kn72bklBc5XTPZ11UYabR//36dd7ZkZ2dj9OjR0hRh6vlfyTSff/65dKeQPd51mZGRgdTUVJ1lEhIS8P777wMA3N3dMWnSpEplatSogbfeegtA2bD4lStXViqTnJyMLVu2ACj7MOzatWtVw7coc+XG0ZgzL4sXL8bq1asBlM0lu2/fPrtd18hceYmPj0dmZqbOeo4dOybV4+LiYtBQYVvi35Jm5spLYmIi8vLytNZRUlKCyZMnS1+yDhs2TO8cvLZmrtw4Oztj1qxZAMqmVig/ZF5NqVTiT3/6E1QqFQBgypQpVQ3fau7du4cDBw4AKLuw7dixo0Gvc/S8AKbn5mn23s57mql5KT/dyttvvy1dR5SnUqnw1ltvSfuGDh1a9YCtqCrnjK754G/evIkRI0ZAqVTCzc0N69atq2qoFtW/f3/UqlULALBhwwacP39eY7mvv/4aX375JQCgadOmJv+N2RNz5saR2sDmyosjtoFzc3MrTMVUXmlpKaZNm4YTJ04AAObNm2cXHZ9xcXHSelzarF69WrrZwc/PD7169bJGaDYlp7zExsbinXfeAQAEBATg0KFDNh0lLafcOBp+N6OZI15L2t/tHFRJdHQ0SkpKMHr0aISEhKBly5Zwd3dHTk4Ojhw5gk2bNklDvnr27Ik///nPNo7YMMePH8e1a9ekn9XHAADXrl2rMHIDgNXuqFbfiefi4oIJEyZY5T3Lq2peMjIy0LdvX4SEhGDYsGHo0KEDvL29AZQN+duzZw/27Nkj9TqvXLlS64is2bNnY9euXfjll18wZ84cXLt2DePGjYO7uzsSExPx0UcfQalUwt3dHWvWrKn6weshp9xcu3YNx48fr/Dco0ePpH+fjmXQoEEWW9hZLnlZt24dlixZAqDsom358uUV7mrRJDAw0GJz7solL/Hx8Rg7diyGDBmC/v37IygoCJ6enigqKsL169exf/9+fPHFF1KDYOHChQgMDDRHCrSSS27kRi552bp1K4YPH47hw4ejT58+CAwMRL169fDo0SOcOXMGMTEx0nRo3t7eWLt2rTkOXye55AYA3nrrLezatQtnz57FkiVLcOXKFURGRsLb2xvXr1/H6tWrkZycDAAICwvD6NGjq3r4Wpm7LbNz507py3FjRwPIKS+AvHJTnr23855mal6ioqKwZs0aXLp0Cd9++y26dOmC6OhodOjQAc7OzkhLS8OGDRukc8bHxwczZswwuH5TyCU3QNnF+d27dzF69Gh06dIFnp6euHv3Lg4fPoyNGzciPz8fTk5OiImJQZs2bYyq21hVzYunpyf+8pe/YOHChXj48CG6d++O6OhovPTSS/Dy8kJ2djb27duHTz/9VGqLfPzxxxrvJpVT+xeQT24crQ1srrzIrQ1sjs+YxMRETJs2DePGjUNoaChatGiBwsJC/Pzzz4iJiZHuyB48eDAWLFigNZasrCwcPHiw0nNqT8fSs2dPtG7dWs8Rmmbx4sWYOXMmRo8ejZ49e8Lf3x916tTBw4cPcf78eezYsUPqhKpRowZiYmIstvYb81JZfHw8Xn/9dQghUK9ePaxduxZ3797F3bt3tb7Gz8/P6HU/jCGX3DgiR/1upqoc8lpSkN3z9fUVAPQ+Ro8eLe7fv2/rcA0WGRlp0HGpH6ZQ587X19eg8pcuXZLeLywszKT3rKqq5iUxMdGg19WqVUts2rRJbzxXr14Vzz77rNZ66tWrJ/bv32+JVFQip9zExsYaFUtiYqKFsiKfvISGhhoVBwCRnp7u8HkxNA53d3exatUqi+XDlJis9TmjTfn3WbRoURWO2DByyYuhcbRv315cvHjRkikxOiZrnTO3b98WnTt31llXWFiYyM/Pt0Q6JOZuywQHBwsAwtnZWWRmZhodj1zyIoT8ciOEY7TznlaVvGRkZIgOHTrojcHPz0+kpqaaeMSGk1NuhgwZovO969evL3bu3GnqoRrFHHlRqVTi7bffFgqFQudrXV1dxYoVK7TGIqf2rxDyyY2jtYHNlRe5tYHNkZfdu3frfI1CoRCvvfaaKCws1BmLoe0i9SM2NtYCGSlj6PdNzZo1E99++61J71H+b0QX5qUyY89bwPKfvXLJjT7p6elSHZGRkSbXY06LFi3S+Xty1O9m9LFWXtTkcM3EETYOYOvWrTh69CiSk5Nx48YN5OTkID8/H3Xq1EHz5s3RvXt3REZGIiQkxNah2r3t27dL2/Y6TUbnzp3x2WefITk5GadPn0ZmZiZycnKgVCrh5eWFoKAg9O/fH5MnT5Z6pHVp3bo1UlNTsX79euzevRvXrl1DcXExmjdvjrCwMEyfPl1aK0juzJ0bR8G8aGauvCxfvhyhoaE4duwYLly4gOzsbNy5cwdOTk6oX78+goKC0K9fP0RERKBJkyZWPELT8ZzRzFx5mTt3Ljp27Ijk5GSkpaXh7t27uHfvHtzc3ODj44MuXbpgzJgxGDVqlN3crWbuc6ZJkyY4efIktmzZgs8//xxpaWnIy8tDgwYN0K1bN0RFRdndumpXr15FSkoKAOCll14y6a50R8wLYJ7cAI7Rziuvqnnx9fXFqVOnsHPnTuzZswdnz57F3bt3IYRA/fr18fzzz2PkyJGIiIiw6J26llDV3MybNw+BgYFISkrCzZs3kZubC09PT/j7+2PEiBGYPHkyGjZsaInQLUKhUGD16tV49dVXsXnzZhw/fhy//vornjx5gjp16qB169YIDQ3Fm2++Kev1eCyBudHMHHlxxDZwr169sGLFCnz//fe4fPkysrOz4eTkhGeeeQZ9+/bFpEmTEBwcbOswjfLNN9/gwIEDOHHiBK5du4bs7Gzk5ubC3d0d3t7e6NixI4YOHYrw8HBpqrzqgHnRjrmxHF5na+aI15IKIf47HoiIiIiIiIiIiIiIiIhsovLEs0RERERERERERERERGRV7LAhIiIiIiIiIiIiIiKyMXbYEBERERERERERERER2Rg7bIiIiIiIiIiIiIiIiGyMHTZEREREREREREREREQ2xg4bIiIiIiIiIiIiIiIiG2OHDRERERERERERERERkY2xw4aIiIiIiIiIiIiIiMjG2GFDRERERERERERERERkY+ywISIiIiIiIiIiIiIisjF22BAREREREREREREREdkYO2yIiIiIiIiIiIiIiIhsjB02RERERERERERERERENsYOGyIiIiIiIiIiIiIiIhtjhw0REREREREREREREZGNscOGiIiIiIiIiIiIiIjIxthhQ0REREREREREREREZGPssCEiIiIiIiIiIiIiIrIxdtgQERERERERERERERHZGDtsiIiIiIio2omLi4NCoYBCoUBUVJStw7GqqKgo6djj4uJsHQ4REREREf0XO2yIiIiIiKqRPn36SF/Wqx/79u0zqo7Zs2dXqmPx4sWWCZiIiIiIiKiaYIcNEREREVE1t23bNoPLlpaWYseOHRaMhoiIiIiIqHpihw0RERERUTWXkJCA+/fvG1T20KFDyMzMtHBERERERERE1Q87bIiIiIiIqqm2bdsCAIqLi7Fz506DXlN+NI769WRf4uLiIISAEKLard9DRERERCRn7LAhIiIiIqqmxo0bB1dXVwCGTYuWn5+P+Ph4AEDHjh3Rvn17S4ZHRERERERUrbDDhoiIiIiommrUqBEGDx4MADh58iSuXr2qs/zu3btRUFAAAIiMjLR4fERERERERNUJO2yIiIiIiKqxiIgIaVvfKBv1fhcXF4wfP97o97p06RLmz5+Pbt26wcfHBzVq1ECjRo0QHByMhQsX4vbt20bVV1paii+++AIREREIDAyEl5cXXF1d0aBBAwQHB2P69Ok4fPgwhBAG1/nll19i2LBhaNGiBdzc3ODt7Y2XX34Zn332mcH1nDlzBkuXLsXQoUPRqlUr1KlTBzVq1ICPjw+6d++OBQsW4LfffjOorpYtW0KhUEChUCAjIwMAcOvWLbz33nvo0KEDPD09Ubt2bbRp0wbR0dH49ddf9dYZFRUl1RkXF6e1nBAC8fHxGD9+PAIDA1GvXj04Ozujdu3aaNmyJfr164e5c+ciMTERKpWq0uszMjKk92nZsqX0fFJSEiZOnIjWrVujVq1a8PDwQJ8+ffCvf/1LY44TExPxyiuv4Nlnn4W7uzu8vb0xZMgQfP3113qPlYiIiIjIrggiIiIiIqo2QkNDBQABQGzYsEEUFRUJLy8vAUC0bNlSqFQqja9LT08XCoVCABBDhgwRQggxduxYqa5FixZpfc/CwkLx5ptvCmdnZ6m8poe7u7tYt26dQcdx7NgxERAQoLM+9WPu3LmVXh8bGyvtj4yMFHl5eWL48OE66xk0aJB48uSJzri6du1qUEyurq5i2bJleo/T19dXek16err48ssvhYeHh84cJiQk6KwzMjJSKh8bG6uxTFZWlggJCTHoWACIQ4cOVaojPT1d2u/r6yuUSqV4++23ddYzadIk6Rx8/PixGDFihM7ys2fP1ptDIiIiIiJ74WJaNw8RERERETmCGjVqYOzYsdi4cSMyMjJw7NgxhIaGViq3bds2afRD+VE5+jx+/BgDBw7EiRMnpOf8/f3RuXNneHl54d69ezhx4gRu376NgoICREdHIz8/H/Pnz9da586dOxEREYGSkhLpuYCAAHTq1AkeHh7Iz8/HxYsXcfHiRahUKhQWFuqMUalUYvTo0Th8+DBq1KiB7t27w9/fH4WFhUhKSpJGwxw8eBAzZszAhg0btNalLuvm5oagoCC0bt0aHh4eEEIgMzMTKSkpyMnJQUlJCebOnQsAmDNnjv5EAvjuu+8wZcoUlJaWokWLFggJCUG9evWQnp6OI0eOQKlUoqCgAOHh4bhw4QL8/PwMqvdppaWlGDJkCM6cOSM9165dO7Rr1w6enp4oLCxEVlYWzp07h8zMTIPrfffdd7FmzRo4OTmha9euaNu2LZRKJZKSkqTRQ7GxsXj22Wcxd+5cjBkzBl9//TVcXFzQo0cPtG7dGk+ePEFiYiKysrIAACtWrMALL7yAcePGmXSsRERERESyYuseIyIiIiIisp6nR9gIIcQPP/wgPffaa69pfF3r1q0FAOHp6SkKCgqEEIaNsImIiJDKBAQEiMTExEpllEql+OSTT4Sbm5sAIJydncUPP/ygsb6zZ8+KmjVrSnV26tRJnDx5UmPZzMxMsWLFCo0jWcqPsFG/7+DBg8WtW7cqlCspKRGzZs2SyioUCpGenq7x/YQQYurUqeLAgQNaR+IolUoRGxsrateuLY20uXHjhtb6yo+wcXNzE7Vr1xbbt2+vNBLqwoULomnTphVGqmijb4RNfHy8tL9JkyZa86t+37lz54qUlJRK+8qPsHF1dRUKhUK0adNGpKamVihXUlJSYeSNl5eXWLJkiQAgevbsWSk/T548EeHh4VL5Vq1aaR0ZRkRERERkT7iGDRERERFRNRcSEoKAgAAAwJ49e1BQUFBh/w8//IBr164BAMLDw1GzZk2D6k1KSpLWvfH398eJEyfQp0+fSuWcnZ0xdepUbNy4EUDZCI/3339fY53R0dHSiJkuXbrg2LFjCA4O1li2cePGmDVrlt4RLEVFRejVqxe++uorNG3atMI+FxcXLF++HF27dgVQtq7Lrl27tNb1ySefICwsDO7u7hr3Ozs7IyoqClu2bAEAlJSUSMetT3FxMfbs2YNXX30VCoWiwr6goCBs2rRJ+nn37t1QKpUG1fu0pKQkafv999/Xml/1+3788cfo1q2bzjpLSkrQsGFDJCYmomPHjhX2ubi4YOXKlQgMDAQA3L9/H4sWLcJzzz2Hb7/9ttJIIXd3d2zevBn169cHANy4cQOnTp0y5hCJiIiIiGSJHTZERERERISJEycCAPLz8xEfH19hn7rTBTBuOrS//e1v0vaqVavQsGFDneWjoqLQpk0bAMA333yD3NzcCvtTUlKkqdUUCgW2bt2KOnXqGByPLmvWrIGLi+YZoxUKBSZNmiT9/OOPP1b5/caMGSPF/t133xn0mqFDh2LQoEFa94eFhaFx48YAgEePHuHSpUsmxZafny9tN2rUyKQ6NJk/f74U39OcnZ0RHh5e4bmlS5dq7fiqW7cuhgwZIv1sjt8JEREREZGtcQ0bIiIiIiLCxIkTsXDhQgghsG3bNvzxj38EUDb6RD2ixN/fHz169DCoPqVSiUOHDgEA6tWrh6FDhxr0ur59++Ly5csQQuDEiRMYPny4tO/gwYPSdv/+/dG2bVuD6tSnVatWeOGFF3SW6dSpk7StXm9Fn59//hmpqanIyMhAfn4+ioqKKuxXj5I5f/48VCoVnJx030/3yiuv6NyvUCjQoUMHaX2XjIwMtG/f3qBYy2vevLm0/emnn2Lo0KFwdnY2up6njRkzRuf+8rG6u7sjLCxMZ/l27dpJ2+np6VULjoiIiIhIBthhQ0RERERE8PX1Re/evXH06FEcOnQIWVlZaNy4Mb766ivk5eUB+N8oHEP8/PPPePz4MQDA1dUV06dPN+h15ae2unnzZoV9J0+elLb79u1rcCz6GNKp0aBBA2m7/AgUTbZu3YqPPvoIv/zyi0HvX1JSggcPHsDLy8uqcWozZswYLF68GCqVCgcOHEC7du3w2muvYfDgwQgKCqo0HZshPDw80KxZM51lyh9/QEAAXF1ddZZXT4kGmH6sRERERERywg4bIiIiIiICUDbd2dGjR1FaWoodO3Zg5syZ0nRoCoXCqA6b27dvS9u5ublYv3690fHcv3+/ws/Z2dnSdqtWrYyuTxsPDw+9Zcp3HpSUlGgsI4TA//3f/yE2NtboGB4+fKi3w8Zccerz3HPPYfny5Zg9ezaEELh8+TLmzJmDOXPmwMvLC927d0doaChGjBghrX2kjyGxl5+Sztjyph4rEREREZGccA0bIiIiIiICUDblVq1atQCUrVtz584daRqynj17GtVJ8uDBgyrHo1QqK/z88OFDadtca9cAMGnEiCaffvpphc6aQYMGYevWrTh//jzu37+PoqIiCCGkh6+vr1RWpVJZLU5DzJw5E4mJiejfv3+F971//z4OHDiAOXPmIDAwEAMGDMD58+f11mds7NY8ViIiIiIiueAIGyIiIiIiAlC2kPvIkSPxr3/9Cz///DPmzp0rdZpEREQYVVft2rWl7eeffx7nzp0zS3xqjx49qnJ95rZy5Uppe8mSJVi4cKHO8uU7oOQoNDQUoaGhyM7OxtGjR3HixAkcP34cP/30k9TBdPjwYQQHB+PQoUMGr29ERERERESacYQNERERERFJynfMxMXFAQBq1qypd8H7p/n4+EjbWVlZZomtfJ1yW2T+5s2buHr1KgDA09MT8+bN01k+Pz+/0pRvcuXj44Pw8HCsXbsWZ86cQVZWFtasWSOtl1NQUIA333zTxlESEREREdk/dtgQEREREZFkwIABaNKkSYXnRowYYdCaIuV17NgRbm5uAIA7d+7g2rVrVY7txRdflLa///77KtdnTuXX7GnTpk2FtWQ0OX78OIQQlg7LIho1aoTp06dj37590nMXL17EjRs3bBgVEREREZH9Y4cNERERERFJnJ2dMWHChArPGTsdGgC4u7ujX79+0s+ffPJJlWMbPHiwtH348GFcunSpynWai5PT/y6tnjx5orf8hg0bLBmOVfTo0QP169eXfs7OzrZhNERERERE9o8dNkREREREVMGCBQtw6tQp6TFw4ECT6pk7d660vW7dOnz33XcGv1bTNGrdunWT1kkRQiAiIkI2a9n4+flBoVAAAC5cuKBztMmuXbuQkJBgrdCMlpOTY1C5vLy8Cvn39va2VEhERERERNUCO2yIiIiIiKgCT09PdOnSRXo4OzubVE9oaCgiIyMBAEqlEkOGDMHSpUu1drIUFhYiPj4eI0aMwPDhwzWW+fvf/y5NtXb69Gn07t0bKSkpGstmZWVh5cqVWLFihUnxG6Nhw4bSlG0qlQpjxozBlStXKpRRqVRYv349Jk6cCGdnZ9SsWdPicZkiPDwcQ4cOxZ49e7SOFvr9998xfvx4FBcXAwACAgLg7+9vzTCJiIiIiByOi60DICIiIiIix7Vp0yZkZmbi22+/RXFxMebPn48PP/wQwcHBaNGiBdzc3JCXl4fr16/jwoULKCoqAgB07txZY30vvPACtmzZgqioKCiVSqSmpuLFF19EYGAgOnXqBA8PDzx48ABpaWm4cOECVCoVpk+fbpVj/eCDD/Dyyy9DpVIhNTUV7du3R48ePdCqVSs8evQISUlJyMzMBAD89a9/RUxMDH799VerxGYMlUqFAwcO4MCBA6hRowaCgoIQEBAADw8PPHz4EL/99huSk5OhUqkAlE2jt3btWhtHTURERERk/9hhQ0REREREFuPm5ob//Oc/WLJkCVatWoUnT57gyZMnSExM1PoaV1dXabSKJhMmTECTJk0wefJkpKenAwCuXLlSaUSLWp06dap2EAbq378/1q9fj+joaCiVSpSUlODIkSM4cuSIVMbJyQnvvvsu5s2bh5iYGKvEZay6detK28XFxUhNTUVqaqrGst7e3oiJicGgQYOsFR4RERERkcNihw0REREREVmUs7Mz3n//fURHR2Pbtm347rvvkJaWhpycHJSUlKBevXrw9fVF+/bt0bdvX4SFhaFRo0Y66+zXrx+uXLmCnTt3IiEhAadPn8adO3dQVFQEDw8PtG7dGiEhIRg1ahR69eplpSMFpkyZgh49emD16tVITEzE7du34e7ujqZNm6Jfv3547bXX0KlTJ6vFY4qvvvoKqampOHz4MFJSUnDp0iXcunULjx8/hpubGxo1aoTnn38eYWFhGD9+POrVq2frkImIiIiIHIJCCCFsHQQREREREREREREREVF15mTrAIiIiIiIiIiIiIiIiKo7dtgQERERERERERERERHZGDtsiIiIiIiIiIiIiIiIbIwdNkRERERERERERERERDbGDhsiIiIiIiIiIiIiIiIbY4cNERERERERERERERGRjbHDhoiIiIiIiIiIiIiIyMbYYUNERERERERERERERGRj7LAhIiIiIiIiIiIiIiKyMXbYEBERERERERERERER2Rg7bIiIiIiIiIiIiIiIiGyMHTZEREREREREREREREQ2xg4bIiIiIiIiIiIiIiIiG2OHDRERERERERERERERkY2xw4aIiIiIiIiIiIiIiMjG/h9QAW0NABIf2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_distances = []\n",
    "idx_groups = []\n",
    "distances_list = []\n",
    "data = pd.DataFrame()  # Initialize an empty DataFrame to store the distances and mechanism labels\n",
    "\n",
    "for idx_group, k in enumerate(grouping):\n",
    "    pattern = r'\\((\\d+), (\\d+)\\)'\n",
    "    match = re.search(pattern, y_df.columns[k-1])\n",
    "    idx_elementary_step_1 = int(match.group(1))\n",
    "    idx_elementary_step_2 = int(match.group(2))\n",
    "    mechanism = (idx_elementary_step_1, idx_elementary_step_2)\n",
    "    print(mechanism)\n",
    "    mechanism_df = data_df[data_df['Mechanism'] == str(mechanism)]\n",
    "    mechanism_x = mechanism_df[conc_list]\n",
    "    scaler = MinMaxScaler()\n",
    "    mechanism_x_transposed = mechanism_x.T\n",
    "    scaled_data = scaler.fit_transform(mechanism_x_transposed)\n",
    "    mechanism_x_scaled = pd.DataFrame(scaled_data.T, columns=mechanism_x.columns, index=mechanism_x.index)\n",
    "    \n",
    "    # Calculate Euclidean distances between each row in true_mechanism_x and mechanism_x_scaled\n",
    "    distances = euclidean_distances(true_mechanism_x, mechanism_x_scaled)\n",
    "\n",
    "    # Flatten the distances array\n",
    "    flat_distances = distances.flatten()\n",
    "\n",
    "    # Append the distances and mechanism labels to the DataFrame\n",
    "    mechanism_labels = [k] * len(flat_distances)\n",
    "    # mechanism_labels = ['Mechanism {}'.format(k)] * len(flat_distances)\n",
    "    temp_df = pd.DataFrame({'Euclidean Distance': flat_distances, 'Mechanism': mechanism_labels})\n",
    "    data = pd.concat([data, temp_df], ignore_index=True)\n",
    "\n",
    "# Create a violin plot\n",
    "plt.figure(figsize=(20, 8))\n",
    "sns.violinplot(x='Mechanism', y='Euclidean Distance', data=data, palette='viridis')\n",
    "\n",
    "# Set x-axis label, adjust font size, and add padding\n",
    "plt.xlabel('Mechanism', fontsize=24, labelpad=10)\n",
    "\n",
    "# Set y-axis label, adjust font size, and add padding\n",
    "plt.ylabel('Euclidean Distance', fontsize=24, labelpad=10)\n",
    "\n",
    "# Adjust x-axis and y-axis tick font size\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Test Case 2: (SM + R -> P, P + R -> IMP2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (min)</th>\n",
       "      <th>SM (mmol)</th>\n",
       "      <th>P (mmol)</th>\n",
       "      <th>IMP2 (mmol)</th>\n",
       "      <th>Time (min).1</th>\n",
       "      <th>SM (mmol).1</th>\n",
       "      <th>P (mmol).1</th>\n",
       "      <th>IMP2 (mmol).1</th>\n",
       "      <th>Time (min).2</th>\n",
       "      <th>SM (mmol).2</th>\n",
       "      <th>...</th>\n",
       "      <th>P (mmol).3</th>\n",
       "      <th>IMP2 (mmol).3</th>\n",
       "      <th>Time (min).4</th>\n",
       "      <th>SM (mmol).4</th>\n",
       "      <th>P (mmol).4</th>\n",
       "      <th>IMP2 (mmol).4</th>\n",
       "      <th>Time (min).5</th>\n",
       "      <th>SM (mmol).5</th>\n",
       "      <th>P (mmol).5</th>\n",
       "      <th>IMP2 (mmol).5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.054883</td>\n",
       "      <td>10.897773</td>\n",
       "      <td>0.048644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.951306</td>\n",
       "      <td>14.787380</td>\n",
       "      <td>0.075807</td>\n",
       "      <td>0</td>\n",
       "      <td>8.315740</td>\n",
       "      <td>...</td>\n",
       "      <td>3.536454</td>\n",
       "      <td>0.026017</td>\n",
       "      <td>0</td>\n",
       "      <td>14.389620</td>\n",
       "      <td>3.401635</td>\n",
       "      <td>0.018203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.133615</td>\n",
       "      <td>5.916848</td>\n",
       "      <td>0.035134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.154893</td>\n",
       "      <td>11.608624</td>\n",
       "      <td>0.050950</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.538506</td>\n",
       "      <td>15.246775</td>\n",
       "      <td>0.082378</td>\n",
       "      <td>1</td>\n",
       "      <td>7.772086</td>\n",
       "      <td>...</td>\n",
       "      <td>4.132100</td>\n",
       "      <td>0.021766</td>\n",
       "      <td>1</td>\n",
       "      <td>13.712021</td>\n",
       "      <td>4.264338</td>\n",
       "      <td>0.020696</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.470655</td>\n",
       "      <td>6.601967</td>\n",
       "      <td>0.033216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.659269</td>\n",
       "      <td>12.304911</td>\n",
       "      <td>0.037308</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.221095</td>\n",
       "      <td>15.653526</td>\n",
       "      <td>0.088615</td>\n",
       "      <td>3</td>\n",
       "      <td>7.568511</td>\n",
       "      <td>...</td>\n",
       "      <td>4.951082</td>\n",
       "      <td>0.023470</td>\n",
       "      <td>3</td>\n",
       "      <td>13.913854</td>\n",
       "      <td>4.418947</td>\n",
       "      <td>0.009280</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.021417</td>\n",
       "      <td>7.069514</td>\n",
       "      <td>0.044141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.751891</td>\n",
       "      <td>14.131892</td>\n",
       "      <td>0.049031</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.489214</td>\n",
       "      <td>16.436043</td>\n",
       "      <td>0.110666</td>\n",
       "      <td>5</td>\n",
       "      <td>7.207536</td>\n",
       "      <td>...</td>\n",
       "      <td>5.277725</td>\n",
       "      <td>0.024900</td>\n",
       "      <td>5</td>\n",
       "      <td>12.333210</td>\n",
       "      <td>4.975389</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.686399</td>\n",
       "      <td>7.463532</td>\n",
       "      <td>0.051491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5.267625</td>\n",
       "      <td>15.320413</td>\n",
       "      <td>0.061309</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.628831</td>\n",
       "      <td>17.123983</td>\n",
       "      <td>0.126293</td>\n",
       "      <td>10</td>\n",
       "      <td>6.211695</td>\n",
       "      <td>...</td>\n",
       "      <td>6.534729</td>\n",
       "      <td>0.033302</td>\n",
       "      <td>10</td>\n",
       "      <td>11.619568</td>\n",
       "      <td>5.968590</td>\n",
       "      <td>0.016615</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.495834</td>\n",
       "      <td>8.450171</td>\n",
       "      <td>0.461695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.0</td>\n",
       "      <td>4.668068</td>\n",
       "      <td>16.240728</td>\n",
       "      <td>0.075154</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.867811</td>\n",
       "      <td>17.573150</td>\n",
       "      <td>0.139235</td>\n",
       "      <td>20</td>\n",
       "      <td>4.969617</td>\n",
       "      <td>...</td>\n",
       "      <td>7.697283</td>\n",
       "      <td>0.052927</td>\n",
       "      <td>20</td>\n",
       "      <td>10.762901</td>\n",
       "      <td>7.035085</td>\n",
       "      <td>0.012860</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.389470</td>\n",
       "      <td>8.701696</td>\n",
       "      <td>0.101093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60.0</td>\n",
       "      <td>3.257470</td>\n",
       "      <td>17.114613</td>\n",
       "      <td>0.101804</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.236235</td>\n",
       "      <td>18.161486</td>\n",
       "      <td>0.174036</td>\n",
       "      <td>30</td>\n",
       "      <td>4.695493</td>\n",
       "      <td>...</td>\n",
       "      <td>8.057533</td>\n",
       "      <td>0.047670</td>\n",
       "      <td>30</td>\n",
       "      <td>10.653514</td>\n",
       "      <td>7.735089</td>\n",
       "      <td>0.019549</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.030617</td>\n",
       "      <td>9.007869</td>\n",
       "      <td>0.214878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.0</td>\n",
       "      <td>2.503555</td>\n",
       "      <td>18.079772</td>\n",
       "      <td>0.116162</td>\n",
       "      <td>119.0</td>\n",
       "      <td>1.639654</td>\n",
       "      <td>18.627261</td>\n",
       "      <td>0.217844</td>\n",
       "      <td>60</td>\n",
       "      <td>3.242582</td>\n",
       "      <td>...</td>\n",
       "      <td>8.935905</td>\n",
       "      <td>0.061670</td>\n",
       "      <td>60</td>\n",
       "      <td>9.912013</td>\n",
       "      <td>8.475547</td>\n",
       "      <td>0.019753</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.030768</td>\n",
       "      <td>8.881478</td>\n",
       "      <td>0.315151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>240.0</td>\n",
       "      <td>1.878292</td>\n",
       "      <td>18.505627</td>\n",
       "      <td>0.135492</td>\n",
       "      <td>239.0</td>\n",
       "      <td>1.302857</td>\n",
       "      <td>18.517978</td>\n",
       "      <td>0.242845</td>\n",
       "      <td>120</td>\n",
       "      <td>2.363729</td>\n",
       "      <td>...</td>\n",
       "      <td>9.755502</td>\n",
       "      <td>0.076568</td>\n",
       "      <td>120</td>\n",
       "      <td>7.577391</td>\n",
       "      <td>8.537024</td>\n",
       "      <td>0.022786</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.012447</td>\n",
       "      <td>8.639504</td>\n",
       "      <td>0.613699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>360.0</td>\n",
       "      <td>1.570946</td>\n",
       "      <td>18.998161</td>\n",
       "      <td>0.152411</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.123043</td>\n",
       "      <td>18.662728</td>\n",
       "      <td>0.262900</td>\n",
       "      <td>240</td>\n",
       "      <td>2.144355</td>\n",
       "      <td>...</td>\n",
       "      <td>10.294027</td>\n",
       "      <td>0.086403</td>\n",
       "      <td>240</td>\n",
       "      <td>8.693213</td>\n",
       "      <td>9.127908</td>\n",
       "      <td>0.027179</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.001343</td>\n",
       "      <td>8.218447</td>\n",
       "      <td>0.915917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>480.0</td>\n",
       "      <td>1.420949</td>\n",
       "      <td>18.690041</td>\n",
       "      <td>0.160391</td>\n",
       "      <td>479.0</td>\n",
       "      <td>1.017155</td>\n",
       "      <td>18.739599</td>\n",
       "      <td>0.239471</td>\n",
       "      <td>360</td>\n",
       "      <td>1.793101</td>\n",
       "      <td>...</td>\n",
       "      <td>11.125382</td>\n",
       "      <td>0.094769</td>\n",
       "      <td>360</td>\n",
       "      <td>8.909109</td>\n",
       "      <td>9.320483</td>\n",
       "      <td>0.025277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480</td>\n",
       "      <td>1.751218</td>\n",
       "      <td>...</td>\n",
       "      <td>10.623232</td>\n",
       "      <td>0.099477</td>\n",
       "      <td>480</td>\n",
       "      <td>9.575231</td>\n",
       "      <td>9.481304</td>\n",
       "      <td>0.022631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time (min)  SM (mmol)   P (mmol)  IMP2 (mmol)  Time (min).1  SM (mmol).1  \\\n",
       "0          0.0  10.054883  10.897773     0.048644           0.0     5.951306   \n",
       "1          1.0   9.154893  11.608624     0.050950           2.0     5.538506   \n",
       "2          3.0   8.659269  12.304911     0.037308           4.0     5.221095   \n",
       "3         10.0   6.751891  14.131892     0.049031           9.0     4.489214   \n",
       "4         20.0   5.267625  15.320413     0.061309          19.0     3.628831   \n",
       "5         30.0   4.668068  16.240728     0.075154          29.0     2.867811   \n",
       "6         60.0   3.257470  17.114613     0.101804          59.0     2.236235   \n",
       "7        120.0   2.503555  18.079772     0.116162         119.0     1.639654   \n",
       "8        240.0   1.878292  18.505627     0.135492         239.0     1.302857   \n",
       "9        360.0   1.570946  18.998161     0.152411         359.0     1.123043   \n",
       "10       480.0   1.420949  18.690041     0.160391         479.0     1.017155   \n",
       "11         NaN        NaN        NaN          NaN           NaN          NaN   \n",
       "\n",
       "    P (mmol).1  IMP2 (mmol).1  Time (min).2  SM (mmol).2  ...  P (mmol).3  \\\n",
       "0    14.787380       0.075807             0     8.315740  ...    3.536454   \n",
       "1    15.246775       0.082378             1     7.772086  ...    4.132100   \n",
       "2    15.653526       0.088615             3     7.568511  ...    4.951082   \n",
       "3    16.436043       0.110666             5     7.207536  ...    5.277725   \n",
       "4    17.123983       0.126293            10     6.211695  ...    6.534729   \n",
       "5    17.573150       0.139235            20     4.969617  ...    7.697283   \n",
       "6    18.161486       0.174036            30     4.695493  ...    8.057533   \n",
       "7    18.627261       0.217844            60     3.242582  ...    8.935905   \n",
       "8    18.517978       0.242845           120     2.363729  ...    9.755502   \n",
       "9    18.662728       0.262900           240     2.144355  ...   10.294027   \n",
       "10   18.739599       0.239471           360     1.793101  ...   11.125382   \n",
       "11         NaN            NaN           480     1.751218  ...   10.623232   \n",
       "\n",
       "    IMP2 (mmol).3  Time (min).4  SM (mmol).4  P (mmol).4  IMP2 (mmol).4  \\\n",
       "0        0.026017             0    14.389620    3.401635       0.018203   \n",
       "1        0.021766             1    13.712021    4.264338       0.020696   \n",
       "2        0.023470             3    13.913854    4.418947       0.009280   \n",
       "3        0.024900             5    12.333210    4.975389       0.013286   \n",
       "4        0.033302            10    11.619568    5.968590       0.016615   \n",
       "5        0.052927            20    10.762901    7.035085       0.012860   \n",
       "6        0.047670            30    10.653514    7.735089       0.019549   \n",
       "7        0.061670            60     9.912013    8.475547       0.019753   \n",
       "8        0.076568           120     7.577391    8.537024       0.022786   \n",
       "9        0.086403           240     8.693213    9.127908       0.027179   \n",
       "10       0.094769           360     8.909109    9.320483       0.025277   \n",
       "11       0.099477           480     9.575231    9.481304       0.022631   \n",
       "\n",
       "    Time (min).5  SM (mmol).5  P (mmol).5  IMP2 (mmol).5  \n",
       "0            0.0     3.133615    5.916848       0.035134  \n",
       "1            1.0     2.470655    6.601967       0.033216  \n",
       "2            3.0     2.021417    7.069514       0.044141  \n",
       "3            5.0     1.686399    7.463532       0.051491  \n",
       "4           10.0     0.495834    8.450171       0.461695  \n",
       "5           20.0     0.389470    8.701696       0.101093  \n",
       "6           60.0     0.030617    9.007869       0.214878  \n",
       "7          120.0     0.030768    8.881478       0.315151  \n",
       "8          240.0     0.012447    8.639504       0.613699  \n",
       "9          360.0     0.001343    8.218447       0.915917  \n",
       "10           NaN          NaN         NaN            NaN  \n",
       "11           NaN          NaN         NaN            NaN  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"Case Studies/Case study 2.csv\")\n",
    "df_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[342, 340, 3]\n",
      "[0.44166666666666665, 0.325, 0.058333333333333334]\n",
      "[340, 342, 177, 343, 3]\n",
      "[0.4, 0.25833333333333336, 0.06666666666666667, 0.058333333333333334, 0.05]\n",
      "[342, 340, 393]\n",
      "[0.49166666666666664, 0.25833333333333336, 0.09166666666666666]\n",
      "[342, 340, 393]\n",
      "[0.45, 0.30833333333333335, 0.06666666666666667]\n",
      "[342, 340, 393]\n",
      "[0.425, 0.35833333333333334, 0.1]\n",
      "[342, 340, 343, 1, 177, 344]\n",
      "[0.35833333333333334, 0.15, 0.125, 0.09166666666666666, 0.058333333333333334, 0.05]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
      "  return bound(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "n_experiments = 6\n",
    "n_species = 3\n",
    "\n",
    "res_probilities, res_prediction, res_original_data, res_interpolated_data = prediction(df_raw, n_species, n_experiments, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [0.44166666666666665, 0.325, 0.058333333333333334], 1: [0.4, 0.25833333333333336, 0.06666666666666667, 0.058333333333333334, 0.05], 2: [0.49166666666666664, 0.25833333333333336, 0.09166666666666666], 3: [0.45, 0.30833333333333335, 0.06666666666666667], 4: [0.425, 0.35833333333333334, 0.1], 5: [0.35833333333333334, 0.15, 0.125, 0.09166666666666666, 0.058333333333333334, 0.05]}\n"
     ]
    }
   ],
   "source": [
    "print(res_probilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [['SM + R -> P', 'P + R -> IMP2'], ['SM + R -> P', 'R -> IMP2'], ['SM -> INT1', 'INT1 -> P + IMP2']], 1: [['SM + R -> P', 'R -> IMP2'], ['SM + R -> P', 'P + R -> IMP2'], ['SM + B + R -> P + B', 'B + R -> IMP2 + B'], ['SM + R -> P', 'SM -> IMP2'], ['SM -> INT1', 'INT1 -> P + IMP2']], 2: [['SM + R -> P', 'P + R -> IMP2'], ['SM + R -> P', 'R -> IMP2'], ['SM -> P + INT1', 'INT1 -> IMP2']], 3: [['SM + R -> P', 'P + R -> IMP2'], ['SM + R -> P', 'R -> IMP2'], ['SM -> P + INT1', 'INT1 -> IMP2']], 4: [['SM + R -> P', 'P + R -> IMP2'], ['SM + R -> P', 'R -> IMP2'], ['SM -> P + INT1', 'INT1 -> IMP2']], 5: [['SM + R -> P', 'P + R -> IMP2'], ['SM + R -> P', 'R -> IMP2'], ['SM + R -> P', 'SM -> IMP2'], ['SM -> INT1', 'INT1 -> P'], ['SM + B + R -> P + B', 'B + R -> IMP2 + B'], ['SM + R -> P', 'SM + P -> IMP2']]}\n"
     ]
    }
   ],
   "source": [
    "print(res_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment #</th>\n",
       "      <th>Mechanism</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.441667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>SM + R -&gt; P,  R -&gt; IMP2</td>\n",
       "      <td>0.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>SM -&gt; INT1,  INT1 -&gt; P + IMP2</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + R -&gt; P,  R -&gt; IMP2</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + B + R -&gt; P + B,  B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>SM + R -&gt; P,  SM -&gt; IMP2</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>SM -&gt; INT1,  INT1 -&gt; P + IMP2</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.491667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>SM + R -&gt; P,  R -&gt; IMP2</td>\n",
       "      <td>0.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>SM -&gt; P + INT1,  INT1 -&gt; IMP2</td>\n",
       "      <td>0.091667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>SM + R -&gt; P,  R -&gt; IMP2</td>\n",
       "      <td>0.308333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>SM -&gt; P + INT1,  INT1 -&gt; IMP2</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>SM + R -&gt; P,  R -&gt; IMP2</td>\n",
       "      <td>0.358333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>SM -&gt; P + INT1,  INT1 -&gt; IMP2</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>SM + R -&gt; P,  P + R -&gt; IMP2</td>\n",
       "      <td>0.358333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>SM + R -&gt; P,  R -&gt; IMP2</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>SM + R -&gt; P,  SM -&gt; IMP2</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>SM -&gt; INT1,  INT1 -&gt; P</td>\n",
       "      <td>0.091667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>SM + B + R -&gt; P + B,  B + R -&gt; IMP2 + B</td>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>SM + R -&gt; P,  SM + P -&gt; IMP2</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Experiment #                                Mechanism  Probability\n",
       "0              0              SM + R -> P,  P + R -> IMP2     0.441667\n",
       "1              0                  SM + R -> P,  R -> IMP2     0.325000\n",
       "2              0            SM -> INT1,  INT1 -> P + IMP2     0.058333\n",
       "3              1                  SM + R -> P,  R -> IMP2     0.400000\n",
       "4              1              SM + R -> P,  P + R -> IMP2     0.258333\n",
       "5              1  SM + B + R -> P + B,  B + R -> IMP2 + B     0.066667\n",
       "6              1                 SM + R -> P,  SM -> IMP2     0.058333\n",
       "7              1            SM -> INT1,  INT1 -> P + IMP2     0.050000\n",
       "8              2              SM + R -> P,  P + R -> IMP2     0.491667\n",
       "9              2                  SM + R -> P,  R -> IMP2     0.258333\n",
       "10             2            SM -> P + INT1,  INT1 -> IMP2     0.091667\n",
       "11             3              SM + R -> P,  P + R -> IMP2     0.450000\n",
       "12             3                  SM + R -> P,  R -> IMP2     0.308333\n",
       "13             3            SM -> P + INT1,  INT1 -> IMP2     0.066667\n",
       "14             4              SM + R -> P,  P + R -> IMP2     0.425000\n",
       "15             4                  SM + R -> P,  R -> IMP2     0.358333\n",
       "16             4            SM -> P + INT1,  INT1 -> IMP2     0.100000\n",
       "17             5              SM + R -> P,  P + R -> IMP2     0.358333\n",
       "18             5                  SM + R -> P,  R -> IMP2     0.150000\n",
       "19             5                 SM + R -> P,  SM -> IMP2     0.125000\n",
       "20             5                   SM -> INT1,  INT1 -> P     0.091667\n",
       "21             5  SM + B + R -> P + B,  B + R -> IMP2 + B     0.058333\n",
       "22             5             SM + R -> P,  SM + P -> IMP2     0.050000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The true mechanism is SM + R -> P, P + R -> IMP2\n",
    "\n",
    "data = {'Experiment #': [], 'Mechanism': [], 'Probability': []}\n",
    "\n",
    "# Iterate through res_prediction\n",
    "for i in res_prediction:\n",
    "    # Iterate through mechanisms and probabilities\n",
    "    for idx, mechanism_list in enumerate(res_prediction[i]):\n",
    "        # print(mechanism_list)\n",
    "        probability = res_probilities[i][idx]\n",
    "        # Join the list elements into a single string\n",
    "        mechanism = ',  '.join(mechanism_list)\n",
    "        # Append data to lists\n",
    "        data['Experiment #'].append(i)\n",
    "        data['Mechanism'].append(mechanism)\n",
    "        data['Probability'].append(probability)\n",
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('res_case_study_2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Euclidean Distance for Predicted Mechanism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouping = [342, 340, 343, 1, 177, 344, 3, 393]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_mechanism_df = data_df[data_df['Mechanism'] == str((7, 163))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
       "      <th>Ea3</th>\n",
       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "      <th>Fast_rxn1</th>\n",
       "      <th>Medium_rxn1</th>\n",
       "      <th>Slow_rxn1</th>\n",
       "      <th>Fast_rxn2</th>\n",
       "      <th>Medium_rxn2</th>\n",
       "      <th>Slow_rxn2</th>\n",
       "      <th>Reaction_order</th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202730</th>\n",
       "      <td>273</td>\n",
       "      <td>0.093891</td>\n",
       "      <td>10</td>\n",
       "      <td>0.011351</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202731</th>\n",
       "      <td>273</td>\n",
       "      <td>0.003292</td>\n",
       "      <td>83</td>\n",
       "      <td>4.818212</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202732</th>\n",
       "      <td>273</td>\n",
       "      <td>0.074048</td>\n",
       "      <td>92</td>\n",
       "      <td>490.984843</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202733</th>\n",
       "      <td>273</td>\n",
       "      <td>9.873814</td>\n",
       "      <td>97</td>\n",
       "      <td>0.011139</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202734</th>\n",
       "      <td>273</td>\n",
       "      <td>0.559118</td>\n",
       "      <td>98</td>\n",
       "      <td>2.656076</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756795</th>\n",
       "      <td>273</td>\n",
       "      <td>0.403615</td>\n",
       "      <td>99</td>\n",
       "      <td>9.441379</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756796</th>\n",
       "      <td>273</td>\n",
       "      <td>9.172688</td>\n",
       "      <td>171</td>\n",
       "      <td>415.203982</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756797</th>\n",
       "      <td>273</td>\n",
       "      <td>535.280431</td>\n",
       "      <td>182</td>\n",
       "      <td>0.010012</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756798</th>\n",
       "      <td>273</td>\n",
       "      <td>327.151844</td>\n",
       "      <td>118</td>\n",
       "      <td>3.529577</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756799</th>\n",
       "      <td>273</td>\n",
       "      <td>695.111736</td>\n",
       "      <td>117</td>\n",
       "      <td>435.108120</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}</td>\n",
       "      <td>(7, 163)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1296 rows × 257 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Temperature          A1  Ea1          A2  Ea2  A3  Ea3  A4  Ea4  \\\n",
       "202730           273    0.093891   10    0.011351   87   0    0   0    0   \n",
       "202731           273    0.003292   83    4.818212  146   0    0   0    0   \n",
       "202732           273    0.074048   92  490.984843  106   0    0   0    0   \n",
       "202733           273    9.873814   97    0.011139   57   0    0   0    0   \n",
       "202734           273    0.559118   98    2.656076  139   0    0   0    0   \n",
       "...              ...         ...  ...         ...  ...  ..  ...  ..  ...   \n",
       "1756795          273    0.403615   99    9.441379   22   0    0   0    0   \n",
       "1756796          273    9.172688  171  415.203982   30   0    0   0    0   \n",
       "1756797          273  535.280431  182    0.010012   86   0    0   0    0   \n",
       "1756798          273  327.151844  118    3.529577   68   0    0   0    0   \n",
       "1756799          273  695.111736  117  435.108120   42   0    0   0    0   \n",
       "\n",
       "         cSM_0s  ...  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  \\\n",
       "202730      0.3  ...           0.0           0.0          0            0   \n",
       "202731      0.3  ...           0.0           0.0          0            0   \n",
       "202732      0.3  ...           0.0           0.0          0            0   \n",
       "202733      0.3  ...           0.0           0.0          0            1   \n",
       "202734      0.3  ...           0.0           0.0          0            1   \n",
       "...         ...  ...           ...           ...        ...          ...   \n",
       "1756795     0.3  ...           0.0           0.0          0            1   \n",
       "1756796     0.3  ...           0.0           0.0          0            1   \n",
       "1756797     0.3  ...           0.0           0.0          1            0   \n",
       "1756798     0.3  ...           0.0           0.0          1            0   \n",
       "1756799     0.3  ...           0.0           0.0          1            0   \n",
       "\n",
       "         Slow_rxn1  Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "202730           1          0            0          1   \n",
       "202731           1          0            1          0   \n",
       "202732           1          1            0          0   \n",
       "202733           0          0            0          1   \n",
       "202734           0          0            1          0   \n",
       "...            ...        ...          ...        ...   \n",
       "1756795          0          0            1          0   \n",
       "1756796          0          1            0          0   \n",
       "1756797          0          0            0          1   \n",
       "1756798          0          0            1          0   \n",
       "1756799          0          1            0          0   \n",
       "\n",
       "                                                 Reaction_order  Mechanism  \n",
       "202730   {'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}   (7, 163)  \n",
       "202731   {'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}   (7, 163)  \n",
       "202732   {'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}   (7, 163)  \n",
       "202733   {'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}   (7, 163)  \n",
       "202734   {'rxn1': {'SM': 1, 'R': 1}, 'rxn_2': {'P': 1, 'R': 1}}   (7, 163)  \n",
       "...                                                         ...        ...  \n",
       "1756795  {'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}   (7, 163)  \n",
       "1756796  {'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}   (7, 163)  \n",
       "1756797  {'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}   (7, 163)  \n",
       "1756798  {'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}   (7, 163)  \n",
       "1756799  {'rxn1': {'SM': 2, 'R': 2}, 'rxn_2': {'P': 2, 'R': 2}}   (7, 163)  \n",
       "\n",
       "[1296 rows x 257 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_mechanism_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
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       "      <th>cINT1_2400s</th>\n",
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       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
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       "</table>\n",
       "<p>1296 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "202730   0.833333  0.830538  0.806130  0.488018  0.336935  0.253812  0.141931   \n",
       "202731   0.833333  0.833238  0.832382  0.814845  0.797421  0.780974  0.736751   \n",
       "202732   0.833333  0.831207  0.812703  0.581206  0.478333  0.424876  0.361274   \n",
       "202733   0.833333  0.617086  0.149387  0.000002  0.000000  0.000000  0.000000   \n",
       "202734   0.833333  0.817551  0.698965  0.310690  0.295336  0.294194  0.294097   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
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       "1756796  0.833333  0.784090  0.577946  0.288870  0.250993  0.233006  0.208341   \n",
       "1756797  0.833333  0.327718  0.123512  0.021612  0.012963  0.009420  0.005289   \n",
       "1756798  0.833333  0.386764  0.154276  0.034227  0.023843  0.019512  0.014296   \n",
       "1756799  0.833333  0.302496  0.147815  0.089319  0.085089  0.083405  0.081487   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  \\\n",
       "202730   0.097754  0.075156  0.061882  ...          0.0          0.0   \n",
       "202731   0.698941  0.666275  0.637798  ...          0.0          0.0   \n",
       "202732   0.342840  0.336670  0.334508  ...          0.0          0.0   \n",
       "202733   0.000000  0.000000  0.000000  ...          0.0          0.0   \n",
       "202734   0.294097  0.294097  0.294097  ...          0.0          0.0   \n",
       "...           ...       ...       ...  ...          ...          ...   \n",
       "1756795  0.346007  0.323306  0.307129  ...          0.0          0.0   \n",
       "1756796  0.197004  0.190166  0.185489  ...          0.0          0.0   \n",
       "1756797  0.003713  0.002871  0.002345  ...          0.0          0.0   \n",
       "1756798  0.012196  0.011023  0.010260  ...          0.0          0.0   \n",
       "1756799  0.080770  0.080391  0.080156  ...          0.0          0.0   \n",
       "\n",
       "         cINT1_3600s  cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  \\\n",
       "202730           0.0          0.0          0.0          0.0          0.0   \n",
       "202731           0.0          0.0          0.0          0.0          0.0   \n",
       "202732           0.0          0.0          0.0          0.0          0.0   \n",
       "202733           0.0          0.0          0.0          0.0          0.0   \n",
       "202734           0.0          0.0          0.0          0.0          0.0   \n",
       "...              ...          ...          ...          ...          ...   \n",
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       "1756798          0.0          0.0          0.0          0.0          0.0   \n",
       "1756799          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "         cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "202730           0.0           0.0           0.0  \n",
       "202731           0.0           0.0           0.0  \n",
       "202732           0.0           0.0           0.0  \n",
       "202733           0.0           0.0           0.0  \n",
       "202734           0.0           0.0           0.0  \n",
       "...              ...           ...           ...  \n",
       "1756795          0.0           0.0           0.0  \n",
       "1756796          0.0           0.0           0.0  \n",
       "1756797          0.0           0.0           0.0  \n",
       "1756798          0.0           0.0           0.0  \n",
       "1756799          0.0           0.0           0.0  \n",
       "\n",
       "[1296 rows x 240 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conc_list = [col for col in true_mechanism_df.columns if col.endswith('s')]\n",
    "true_mechanism_x = true_mechanism_df[conc_list]\n",
    "scaler = MinMaxScaler()\n",
    "true_mechanism_x_transposed = true_mechanism_x.T\n",
    "scaled_data = scaler.fit_transform(true_mechanism_x_transposed)\n",
    "true_mechanism_x = pd.DataFrame(scaled_data.T, columns=true_mechanism_x.columns, index=true_mechanism_x.index)\n",
    "true_mechanism_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unrelated_groups = [group for group in range(1, 457) if group not in grouping]\n",
    "# print(unrelated_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[342, 340, 343, 1, 177, 344, 3, 393, [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 341, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456]]\n"
     ]
    }
   ],
   "source": [
    "all_groups = [342, 340, 343, 1, 177, 344, 3, 393, unrelated_groups]\n",
    "print(all_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 163)\n",
      "(7, 126)\n",
      "(7, 334)\n",
      "(0, 146)\n",
      "(29, 179)\n",
      "(7, 382)\n",
      "(0, 154)\n",
      "(8, 138)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/opt/py/conda/PyLib_Common/envs/boda2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_distances = []\n",
    "idx_groups = []\n",
    "distances_list = []\n",
    "data = pd.DataFrame()  # Initialize an empty DataFrame to store the distances and mechanism labels\n",
    "\n",
    "for idx_group, k in enumerate(grouping):\n",
    "    pattern = r'\\((\\d+), (\\d+)\\)'\n",
    "    match = re.search(pattern, y_df.columns[k-1])\n",
    "    idx_elementary_step_1 = int(match.group(1))\n",
    "    idx_elementary_step_2 = int(match.group(2))\n",
    "    mechanism = (idx_elementary_step_1, idx_elementary_step_2)\n",
    "    print(mechanism)\n",
    "    mechanism_df = data_df[data_df['Mechanism'] == str(mechanism)]\n",
    "    mechanism_x = mechanism_df[conc_list]\n",
    "    scaler = MinMaxScaler()\n",
    "    mechanism_x_transposed = mechanism_x.T\n",
    "    scaled_data = scaler.fit_transform(mechanism_x_transposed)\n",
    "    mechanism_x_scaled = pd.DataFrame(scaled_data.T, columns=mechanism_x.columns, index=mechanism_x.index)\n",
    "    \n",
    "    # Calculate Euclidean distances between each row in true_mechanism_x and mechanism_x_scaled\n",
    "    distances = euclidean_distances(true_mechanism_x, mechanism_x_scaled)\n",
    "\n",
    "    # Flatten the distances array\n",
    "    flat_distances = distances.flatten()\n",
    "\n",
    "    # Append the distances and mechanism labels to the DataFrame\n",
    "    mechanism_labels = [k] * len(flat_distances)\n",
    "    temp_df = pd.DataFrame({'Euclidean Distance': flat_distances, 'Mechanism': mechanism_labels})\n",
    "    data = pd.concat([data, temp_df], ignore_index=True)\n",
    "\n",
    "# Create a violin plot\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.violinplot(x='Mechanism', y='Euclidean Distance', data=data, palette='viridis')\n",
    "\n",
    "# Set x-axis label, adjust font size, and add padding\n",
    "plt.xlabel('Mechanism', fontsize=24, labelpad=10)\n",
    "\n",
    "# Set y-axis label, adjust font size, and add padding\n",
    "plt.ylabel('Euclidean Distance', fontsize=24, labelpad=10)\n",
    "\n",
    "# Adjust x-axis and y-axis tick font size\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(res_original_data, res_interpolated_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
